The cost was immense with an estimated 99% fall in the North American marketplace activity, or 6.3 million lost orders. While Amazon attributed the disruption to a “software code deployment,” internal reports identified a more systemic culprit: AI-assisted changes implemented without established safeguards. Amazon is not the first and it will not be the last.

——

Across organizations, we have all felt the push to adopt new AI OKRs and increase business efficiency. In software engineering, this has produced incredible throughput increases, with McKinsey reporting productivity increase of 20-45% for teams that adopted AI coding tools early. 

However, this revolution in how we write code has incurred a massive stability debt. Google’s 2025 DORA report, noted a concerning 10% increase in software instability reported alongside AI adoption. This article explores how Runtime-aware development, a strategy grounding AI agents in execution-level reality can prevent these high-impact incidents from occurring.

The Link Between Velocity and Incidents

The greatest predictor of an outage is change. Production incidents are frequently traced back to a specific code modification. The greater the rate of change, the higher the risk that an error will occur; if governance does not increase at the same rate as velocity, this risk increases unchecked.

While this was true in human engineering, AI-acceleration has fundamentally altered this risk equation. The risk of any automated action is a product of two forces: Velocity and the potential Blast Radius, divided by the effectiveness of your Governance.

Backed by AI code agents like Cursor, GitHub Copilot, and others, we have optimized for velocity, merging PRs faster than ever before. But this amplification has been achieved without a corresponding increase in our ability to validate those changes, and mitigate against unforeseen blast radius. 

This “velocity-to-incident” chain catches companies like Amazon, and it is the primary threat to any organization scaling AI automation today.

The Shift to Non-Deterministic Failure

Traditional software development was deterministic. A human developer had a clear intent and wrote specific lines of code they knew would generate a required output. Faced with the same challenge twice, they would produce more or less the same logic each time.

AI agents work on an entirely different methodology. They do not follow a static rulebook. Instead, when faced with a problem, they calculate the highest probability path to reach a goal. Because it is a probabilistic engine, an agent can write a slightly different solution every single time it is asked. 

This introduces a category of “unknown-unknowns” into our running systems. While AI agents accelerate code generation, they lack the human developer’s inherent understanding of how that code will fit into the live environment. Software is becoming easier to write, but harder to understand once it runs.

The Three Levels of AI Awareness

To understand why AI is struggling, we have to look at how it views your system. Most AI agents operate with only two levels of awareness, leaving them blind to the third:

1. Local Context: Visibility of the immediate file. This is great for syntax and logic but blind to the rest of the architecture.
2. Global Context: Awareness of the entire repository. This enables architectural consistency but remains static. This reflects what the code is, not how it behaves.
3. Runtime Context: The ground truth of the live, running application. This provides the variables, call stacks, and real traffic patterns necessary to move from probabilistic guessing to deterministic validation.

Without Level Three, AI agents are forced to navigate by an idealized map that rarely matches the actual road.

The Amazon Post-Mortem

On March 10, 2026, Amazon apparently convened an engineering “deep dive” to address a trend of incidents with a “high blast radius.” 

• March 2, 2026: Incorrect delivery times were shown, leading to 120,000 abandoned orders and 1.6 million website errors.
• March 5, 2026: A total storefront blackout reportedly triggered by an engineer following inaccurate advice inferred by an AI agent from an outdated internal wiki.

In both incidents the bug was discovered in production, but it could have been identified hours earlier before the incident occurred. The AI agent just needs access to runtime insights during the authoring phase. 

Without it, agents are “flying blind,” making decisions that are hypothetically optimal in a vacuum but operationally disastrous in the real world.

The Productivity Paradox: Why Senior Sign-Offs Fail

The response to such outages is to mandate senior engineer oversight for all AI-assisted changes. While a prudent immediate safeguard, it creates a massive productivity paradox: 

• The Bottleneck: It adds significant toil to your most expensive talent, completely negating the velocity gains AI was supposed to provide. 
• Automation Bias: Humans are statistically less likely to catch logic-based errors in machine-generated code, which can contain significantly more logic flaws than human code, because the output looks syntactically perfect. 

We cannot solve a machine-speed problem with a manual-speed process. 

The Runtime Aware Evolution: Simulating Reality

For the last decade, we focused on Shift Left, moving testing earlier in the SDLC. But in the age of AI, we can move as far “left” as we want; if our agents only see source code, they are still running blind.

To safely harness AI speed, we have to adopt runtime aware development and validation. We can connect the AI code agent’s reasoning loop directly to the runtime across all environments, from QA and Staging to Pre-production, and our AI SRE (site reliability engineering) tools to confirm that these changes do not negatively impact downstream dependencies and third-party integrations.

By giving AI agents visibility to runtime awareness they can preview and simulate the runtime impact of a change before it reaches scale. It allows the AI to ask: 

“If I apply this logic to the current live traffic pattern, what happens to the call stack?” This “ground truth” feedback loop prevents hazardous hallucinations before they ever leave the authoring stage.

Moving Reliability into the Authoring Phase

By moving to a runtime-aware model, we move reliability from a reactive activity into proactive authoring. We connect the AI code agent reasoning loop directly to the runtime, across all environments, from QA, Staging, Pre-production and Production.

This provides the AI with the “sensors” it needs to understand the ground truth of the system before the first line of code is ever committed.

• Shift left: Focuses on when we verify code. It ensures code is syntactically correct and passes unit tests before merging.
• Runtime aware: Upgrades validation with behavioral context. It enables AI agents to confirm their logic against a live execution layer early in the SDLC.

By grounding AI agents in execution-level reality across all environments, we can ensure that code validation is based on how the system actually works, not just how the “map” of the source code looks.

The Solution: Lightrun MCP and Production-Grade Engineering

Lightrun connects AI assistants directly to live software environments, acting as the interface between the AI brain and the live runtime. 

Lightrun MCP enables AI-accelerated runtime aware development by: 

• Simulating runtime impact: Enabling AI agents to preview and simulate exactly how a code change will behave using a read-only sandboxed running environment.

• Validating non-deterministic logic: Verifying AI-suggested changes and code optimizations against real-world data patterns before they reach scale.

• Mitigating outages early: Identifying “Sev2” incidents and logic errors at the authoring stage rather than during an active incident response. 

• Empowering Agents with runtime context: Through our MCP, Lightrun provides AI agents with the real-time visibility to understand environmental variables, preventing destructive hallucinations that plague context-blind agents. 

In the AI era, the critical capability isn’t just generating code faster. It’s seeing, preventing, and fixing what happens at runtime when that code meets reality.

Stop flying blind. Equip your AI agents with live runtime context today.

The post The Amazon Outage Is a Warning. Is Your AI Agent Flying Blind? appeared first on Lightrun.

Support engineering teams frequently face a “visibility gap”: vague customer reports and incomplete data that lead to dreaded “cannot reproduce” (CNR) bugs. To scale, support must diagnose root causes in minutes without constantly escalating to developers.

In this article, we explore how to equip support engineers with the tools to achieve technical certainty at scale by leveraging runtime context and AI-driven reasoning.

————

The High Cost of “Cannot Reproduce”

In Support Engineering, cannot reproduce is the most expensive phrase we can hear. It represents exhausted capacity, frustrated customers, and shattered developer focus.

Studies suggest that 17% of tickets are closed after being marked “cannot reproduce.” When this happens:

• The customer stays frustrated,
• The Support Engineer loses credibility,
• And the developer’s focus is shattered by hunting for an issue that cannot be found.

Beyond immediate friction, these bugs create a hidden liability. They remain in production, increasing the “blast radius” of potential failures as more users hit the same edge case. As software instability climbs by nearly 10% as a follow-on consequence of AI-accelerated development, our investigation methods must evolve.

What is the Reproduction Tax?

The Reproduction Tax is the wasted engineering capacity spent trying to mimic production behavior in local or staging environments. Organizations that eliminate this tax can reduce Mean Time to Resolution (MTTR) from hours to minutes.

Why Modern Incident Resolution Is Broken?

Bugs discovered in production are up to 600% more expensive to resolve than those found during development. Traditional data-gathering methods are fundamentally broken, leading to several key operational bottlenecks that we face as team leads:

• Environmental Mismatch: Production failures often rely on specific traffic patterns or data states that staging simply cannot mirror. This gap extends downtime, negatively impacting the customer experience and bleeding company revenue.

• Context gaps: Teams lack runtime context. This forces support into a cycle of reactive firefighting and guesswork. This forces support into a cycle of reactive firefighting, wasting engineering time on guesswork instead of innovation.
• AI agent limitations: AI agents face the same limitations as human engineers in incident resolution. Without runtime data, the AI’s effectiveness depends entirely on how well an engineer can guess the correct context in a prompt.
• Manual Data Collection: Legacy debugging requires new deployments just to collect diagnostic data. Every log-and-redeploy cycle inflates observability costs with limited ROI for the business.
• Developer dependency: Support teams are frequently paralyzed for days waiting on developer bandwidth to investigate tickets. This delay strains client relationships and increases the likelihood of churn. It also forces developers to prioritize tedious reproduction cycles over high-value feature development.

The Reality: Staging is not production. Modern distributed systems are too complex to replicate. We are forced to investigate live failures using static, historic logs rather than the active state of the system.

The 8-hour investigation: A best-case scenario

Even a simple “file upload failure” can consume a full engineering day. Compare the traditional workflow to an autonomous one:

• Report: A support engineer receives a report from a customer describing a failure.
• Context collection: We follow up to collect essential diagnostic information (e.g., file type, widespread vs local).
• Hypothesis testing: We try to eliminate potential causes by guessing at environmental variables (e.g., “does it only crash when the list exceeds 100 rows?”).
• Escalation: We escalate to engineering to check logs. If data was not captured, the trail goes cold. 
• Redeploy: We have to redeploy code changes just to see if the root cause can be captured. 
• Repeat: If the cause of the incident was not found, the cycle repeats.

The solution: AI-driven reasoning and Runtime context

To remove this lengthy process, we need to adopt autonomous issue resolution. We created AI site reliability engineering tools (AI SREs) for this purpose, but they need two core capabilities:

• AI-driven reasoning: The ability to analyze and correlate data across multiple observability vendors, APIs, and databases.
• Runtime Context: The “Source of Truth.” Instead of recreating a failure, teams capture the complete failure state where it happened.

We built Lightrun AI SRE with these two principle. Combining them support teams can understand issues, find and test mitigation suggestions and resolve many incidents without escalation to developer teams.

Introducing Lightrun AI SRE: Reliability across the SDLC

Reliability cannot start after an incident occurs. It must extend across the entire software development lifecycle. Engineers should be able to ask questions about their systems as they work, and support teams should be able to query behavior the moment it is flagged.

We built Lightrun AI SRE to transform investigations into a unified analysis layer. Because AI cannot resolve what it cannot see, Lightrun observes the system as it runs, safely instrumenting live environments without a redeploy.

Since AI cannot resolve what it cannot see, Lightrun AI SRE observes the system as it actually runs, safely instrumenting running systems and correlating context from multiple perspectives, unifying logs, metrics, traces, infrastructure signals, and change history.

By grounding this analysis in live execution state, Lightrun provides root cause analysis with a level of confidence that static code or disconnected logs simply cannot match.

This fundamentally changes how we can ensure reliability for our customers:

• Instant system understanding: Lightrun AI SRE explains how the system works, answers behavior questions, and clarifies configuration and architecture. This translates into fewer escalations, faster MTTR, and smoother customer onboarding.
• Failure classification: It enables faster triage and reduces the false escalations that clutter developer backlogs by distinguishing real bugs from setup or environment issues. 
• Intelligent incident routing: By identifying whether an issue is application-level, infrastructure-related, or dependency-driven, it routes the incident to the relevant team immediately. This eliminates the “ping-pong” between departments and ensures clear ownership.
• Strengthen resilience: It provides tested, actionable remediation suggestions and improves long-term resilience with automated postmortems that ensure that the resolution of one ticket can prevent the next ten.

Case study: The bug that was actually a hard limit

In our own work here at Lightrun, we test out our own product whenever we face engineering issues. I like to approach root cause analysis like a detective, collecting all the evidence I can from logs, telemetry, code, and recent changes.

We had an interesting use case with a client. The issue turned out to be simple, but finding it was a real challenge. One of our APIs was returning an unexpectedly small amount of records to one of our customers, and they could not see all their entities in the plugin interface.

Initially, we were stuck. The API appeared functional and the database returned results, but something was clearly wrong. When we tried to reproduce it locally, everything worked perfectly. It turned out our local test data didn’t hit the specific volume thresholds present in production, allowing the bug to remain hidden.

We were building Lightrun AI SRE so we tested it out. It was only when we dived into the client’s production environment that the truth came out. Collecting live runtime context, the AI agent placed a snapshot right after the database query but before the return to the user. Without escalating to a developer, it set an expression to compare the rows returned from the database vs. those from the API, and the live context revealed the discrepancy instantly: 

The client was calling 100 records from the database, but the API could only return 20 due to a wrong REST controller configuration.

It wasn’t a ghost; it was a hard-limit annotation in the code.

While the root cause was visible in the code once we knew where to look, the “killer feature” was being able to get the data to prove it without a single reproduction attempt. Having the AI SRE agent dynamically collect this evidence from the actual environment satisfying gave us the technical certainty to implement a long-term solution, using search and pagination, in minutes. Impressively we could do all of this without a single redeploy.

The new support investigation workflow

Using Lightrun AI SRE we’ve adopted an automated five-step workflow that lets our team work without the Reproduction Tax:

1. Triage automatically: Lightrun AI SRE connects reports to live system signals directly within tools like Slack or PagerDuty to determine if a behavior is a user error or a bug.
2. Assess impact: It identifies failing services and the exact percentage of users impacted in real-time.
3. Eliminate unknowns: The AI agent dynamically tests hypotheses by correlating runtime snapshots and environment variables to rule out common culprits like configuration drifts or local data mismatches.
4. Prove root cause: It investigates directly in production systems to capture live variable values without manually checking code.
5. Convert to knowledge: We then take all the investigation details, automatically update the initial Jira ticket to explain the event to engineers alongside technical proof like logs and snapshots.
6. Propose and validate fixes: We can then suggest product changes to prevent recurrence, and using the Lightrun MCP the AI code agent engineers can generate and validate the changes, before updating the customer of a successful resolution.

Switch to 4-minute incident resolution

Our goal isn’t just to close tickets faster, it’s to end the “Ping-Pong” between departments that wastes our engineering bandwidth. By removing the Reproduction Tax, enterprise teams at organizations like Taboola and AT&T have achieved a 90% reduction in MTTR.

When your team has access to live production evidence, you move beyond the limits of reactive triage. You stop simply routing tickets and start delivering the technical certainty required to resolve incidents in minutes, not days.

The post How to Solve “Cannot Reproduce” Bugs That Cost Support Teams Hours appeared first on Lightrun.

TL;DR

• AI accelerated code generation, but not incident resolution. Modern failures emerge under real traffic, not in staging environments.
• MTTR is now limited by runtime verification, not development speed. Faster deployment pipelines do not confirm root causes faster.
• Pre-collected telemetry is structurally incomplete. Logs, metrics, and traces are predefined. If the triggering runtime state was never captured, debugging or incident resolution becomes guess-driven.
• AI SRE improves correlation but cannot prove execution without runtime context. It narrows hypotheses but does not validate them.
• Significant MTTR reduction requires runtime-verified diagnosis. Generating execution-level evidence on demand eliminates redeploy loops and directly confirms root causes.

Production failures rarely appear where teams expect them. A deployment passes review, automated tests succeed, and staging environments appear stable. Under real traffic, latency spikes. Logs show no explicit error. Metrics are aggregated. Traces reveal only fragments of execution paths. Engineers add instrumentation, redeploy, and attempt reproduction, repeating the cycle until the root cause is confirmed.

AI has dramatically increased development velocity. Code generation, review, and deployment pipelines operate faster than ever. The bottleneck has shifted. Writing code is no longer the limiting factor; verifying how it behaves in live runtime conditions is.

AI SRE (AI site reliability engineering) is emerging to address this operational shift. AI systems can correlate signals across services, summarize incidents, and suggest likely remediation paths. Yet most AI SRE implementations remain constrained by pre-captured telemetry. If the critical runtime state was never recorded, AI can infer patterns but cannot verify what was actually executed. Sustainable MTTR reduction in modern systems requires moving beyond telemetry interpretation toward runtime-verified diagnosis.

What Actually Expands MTTR in Cloud-Native Systems?

In modern microservices architectures, MTTR rarely increases because engineers cannot write fixes. It increases because teams cannot quickly confirm what happened.

When a latency spike breaches an SLO, incident response begins immediately. Engineers pivot across dashboards, logs, traces, deployment history, and configuration changes to determine what shifted. In distributed systems, signals are fragmented. Logs are predefined. Metrics are aggregated. Traces show partial paths.

If the runtime condition that triggered the failure was never captured, teams must redeploy with additional instrumentation and attempt to reproduce the failure under live traffic. Each cycle adds delay. In practice, confirmation, not hypothesis generation, dominates MTTR.

How Does AI SRE Compress the Investigation Window?

AI SRE operates inside this investigative loop. Instead of engineers manually toggling between tools like Grafana, Datadog, Splunk, and distributed tracing systems, AI models analyze telemetry in parallel. 

They correlate anomalies with recent deployments, configuration changes, and traffic spikes to rapidly narrow the likely fault domain.

AI SRE helps teams:

• Correlate alerts across distributed services
• Detect abnormal patterns in logs and metrics
• Map failures to recent releases
• Prioritize incidents by SLA/SLO impact
• Generate probable root cause hypotheses
• Recommend remediation actions
• Produce structured RCA summaries

This significantly reduces the time spent narrowing the search space. However, most AI SRE systems still rely on pre-captured telemetry. If the relevant runtime state was never logged, AI can suggest probable causes but cannot verify execution. Correlation accelerates triage, but confirmation may still require redeploy cycles.

To materially reduce MTTR, AI must move from correlation to runtime-aware diagnosis.

AI SRE vs Traditional SRE: Impact on MTTR Reduction

Traditional SRE focuses on detection and human-led investigation. When an alert fires, engineers manually check dashboards, logs, traces, and deployment history to determine what changed. They compare error spikes in Grafana, scan logs in Splunk or Datadog, inspect distributed traces, review recent commits, and check configuration updates.

If the runtime condition that caused the failure was never captured, engineers must add new instrumentation, redeploy the service, and attempt to reproduce the issue under similar traffic conditions. Each iteration increases the team’s MTTR.

AI SRE accelerates this workflow by automating cross-telemetry correlation. Instead of sequential analysis, these AI systems scan logs, metrics, traces, deployment history, and configuration changes in parallel. They identify anomalous patterns, map impacted services, and generate probable fault domains within seconds, significantly reducing time spent narrowing the search space.

The material shift occurs when AI moves from correlation to verification. Telemetry-driven AI narrows possibilities. Runtime-aware AI confirms execution. That distinction determines how quickly an investigation concludes and how effectively MTTR reduction can be achieved.

The contrast between correlation and verification becomes clearer in a real production incident.

Correlation vs Execution Proof in a Real Production Incident

Consider a payment service experiencing a sudden spike in latency after a deployment.

A telemetry-driven AI SRE system correlates the spike with updated retry logic and flags a potential regression. The hypothesis is statistically strong, but it remains inferential. Engineers still need to redeploy with additional logging to confirm whether the retry branch actually executed under real traffic.

A runtime aware AI SRE system generates execution-level evidence immediately. It inspects the live conditional branch, confirms that the retry loop triggered under specific inputs, and validates the remediation before broad rollout. No speculative redeploy is required.

The operational difference is clear:

• Correlation narrows possibilities.
• Verification confirms reality.
• Confirmed root causes eliminate the need for repeated investigation loops.

Eliminating redeploy cycles directly reduces MTTR and increases confidence in remediation decisions.

In complex distributed architectures, investigation time is dominated by validation rather than hypothesis generation. Telemetry-driven AI accelerates pattern recognition, but runtime-aware AI SRE shortens the path to confirmed resolution. By generating missing execution evidence on demand and validating fixes before impact, AI SRE eliminates redeploy loops and reduces MTTR through execution-level certainty rather than probabilistic inference.

The Runtime Context Gap: Why Telemetry-Driven AI SRE Falls Short

Telemetry-driven AI SRE inherits the limitations of modern observability systems. Logs, metrics, and traces are designed to capture predefined signals, not every possible runtime condition. Logs reflect what engineers anticipated might matter. Metrics summarize behavior at an aggregate level. Traces follow selected execution paths. None of these guarantees that the exact signal required to explain a specific failure is available when an incident occurs.

When critical runtime information is missing, investigation slows down. Teams typically redeploy to add new logging, wait for CI/CD pipelines to complete, attempt to reproduce the issue locally, or infer behavior from incomplete data. Each iteration increases Mean Time To Resolution (MTTR) and introduces uncertainty. 

AI models trained solely on telemetry can identify patterns, but they cannot verify what actually happened during execution. This gap defines the structural limitation of telemetry-driven AI SRE. 

Without direct visibility into execution paths, variable state, conditional logic, dependency behavior, and traffic-specific inputs, AI remains correlation-based rather than verification-based. To move beyond inference, AI SRE requires access to live runtime context.

This runtime context gap is not an implementation flaw; it is an architectural boundary. As long as AI operates only on predefined telemetry, MTTR reduction will plateau at the speed of correlation. Sustainable MTTR reduction requires grounding AI systems in live execution behavior.

Why Runtime Context Is the Foundation of Sustainable MTTR Reduction

AI SRE becomes reliable only when intelligence is grounded in live execution behavior. Systems that rely solely on predefined logs and metrics can identify patterns and generate hypotheses, but they cannot confirm what actually occurred during runtime. Verification requires direct access to execution paths, variable state, and conditional logic under real traffic conditions.

Lightrun enables this shift by allowing teams and AI agents to generate execution-level evidence inside running services without redeployment. Dynamic logs, snapshots, and metrics can be injected safely, providing immediate visibility into live behavior. Instead of waiting for additional telemetry or repeating investigation cycles, teams validate root causes directly against the runtime.

This transition moves reliability engineering from correlation-based analysis to verification-based diagnosis. Correlation accelerates investigation. Runtime context concludes it. In complex distributed systems, a confirmed execution state materially reduces MTTR.

AI SRE in Practice: Core Use Cases

AI SRE delivers measurable value by improving how teams diagnose, resolve, and prevent production failures. When grounded in live execution behavior, these workflows drive meaningful reductions in MTTR across distributed systems. The following workflows illustrate how runtime-aware AI SRE reduces MTTR across enterprise systems.

1. Runtime-Aware Root Cause Analysis and Fix Recommendations

Root cause analysis in distributed systems often involves multiple services and recent deployments. Consider a payment processing system where checkout latency increases only for a subset of customers. 

Telemetry shows increased response times, but no obvious error logs. AI SRE correlates the latency spike with a recent retry-logic deployment and identifies a potential loop under specific input conditions.

When the runtime context is available, AI SRE can inspect the exact execution path and variable state that triggered the retry loop. Instead of suggesting a probable fix, the system validates the condition under real traffic and confirms the remediation before full rollout. This reduces regression risk, shortens MTTR, and increases confidence in the fix.

2. Alert Triage and Routing Across Teams and Services

Large organizations operate dozens or hundreds of microservices owned by different teams. When an authentication service degrades, alerts may cascade across API gateways, billing services, and user-facing applications. Traditional routing rules often lead to multiple teams investigating the same issue.

AI SRE correlates alerts across services, identifies the originating component, and automatically maps ownership. With runtime evidence attached, the system can show that failed token validation requests originate from a misconfigured identity provider rather than downstream services. Accurate routing reduces escalation chains and allows the responsible team to act immediately.

3. Deep Research Across Code and Environment Layers

Enterprise outages frequently span application logic, infrastructure conditions, and third-party dependencies. Consider a SaaS platform experiencing intermittent data inconsistencies across multiple regions. Logs indicate successful writes, but users report stale reads.

AI SRE, supported by runtime context, can analyze execution flows across write services, caching layers, and replication pipelines. By inspecting variable state and dependency behavior under live traffic, the system identifies a cache-invalidation timing issue specific to a single region. This cross-layer visibility enables resolution without relying solely on telemetry summaries.

4. Dynamic Instrumentation for Unknown Unknowns

Unknown unknowns extend the incident duration because the required signal was never anticipated. On a high-frequency trading platform, an intermittent pricing discrepancy can occur under specific market conditions. Existing telemetry does not capture the intermediate calculation state that produced the error.

AI SRE enables dynamic instrumentation within the running service to capture the state of missing variables without redeploying the application. By gathering targeted runtime evidence, the system isolates a rounding condition triggered by rare input combinations. Eliminating redeploy cycles reduces investigation time in latency-sensitive environments.

5. Post-Mortems and Knowledge Capture

Enterprise systems must learn from incidents to prevent recurrence. On a healthcare platform handling regulated data, a configuration change causes intermittent authorization failures. The incident is resolved, but without validated runtime evidence, post-mortems risk documenting inferred causes.

AI SRE, grounded in runtime context, captures the exact execution conditions that produced the failure, including configuration state and request attributes. This verified evidence strengthens compliance documentation and prevents similar regressions in future releases. Reliability evolves from reactive response to structured learning.

Lightrun Hands-On: Detecting a Logic Bug Without Redeploy

To illustrate how runtime context changes debugging workflows, consider a real scenario involving a Java Spring Boot trading application. The system processes buy and sell orders and runs two environments simultaneously: a production instance on port 8130 and a pre-production instance on port 8135. Both environments start with the Lightrun agent attached at JVM startup, allowing engineers and AI assistants to inspect runtime behavior without modifying or redeploying the service.

The trading workflow is implemented in the TradeExecutionService.executeTrade() method, which processes every incoming trade request. This method retrieves the current market price for a symbol, calculates the trade’s total cost, determines whether the trade qualifies as a high-value transaction, and then forwards the request to the fraud validation service.

The Subtle Bug in Trade Validation

The system attempts to flag trades above $10,000 so the fraud validation service can apply stricter checks.

// Calculate total cost
BigDecimal totalCost = currentPrice.multiply(BigDecimal.valueOf(request.getQuantity()));

if (totalCost.compareTo(BigDecimal.valueOf(10000)) > 0) {
    try {
        java.lang.reflect.Field field = TradeRequest.class.getDeclaredField("highValue");
        field.setAccessible(true);
        field.set(request, "true");  // incorrect type
    } catch (Exception e) {
        log.error("Error setting high value flag: {}", e.getMessage());
    }
}

At first glance, the logic appears correct, but the bug lies in a small type mismatch. The TradeRequest.highValue field is declared as a boolean, yet the reflection code attempts to set the value “true” as a String.

This mismatch throws an IllegalArgumentException. Because the exception is caught and logged, the error never propagates, and the high-value flag is never set. As a result, trades above $10,000 pass through fraud validation without triggering stricter rules.

The Traditional Debugging Loop

Without runtime inspection, diagnosing this issue would require a full redeploy cycle:

• Add temporary debug logs inside the high-value logic
• Rebuild the application JAR
• Redeploy the service
• Reproduce a trade above $10,000
• Inspect logs and iterate again if needed
  

In distributed systems with multiple environments, each redeployment interrupts the running system and increases investigation time.

Investigating the Issue Using Cursor + Lightrun MCP

Instead of modifying the source code, the investigation can be performed safely directly in the IDE using the Cursor with Lightrun MCP. MCP provides the AI assistant with access to runtime context, allowing it to dynamically request telemetry from a sandboxed, read-only running service.

The engineer asks the assistant to inspect runtime behavior for trades exceeding $10,000 in the executeTrade() method.

Step 1: Inspect the Method in Cursor

The engineer opens the TradeExecutionService file in Cursor and asks the assistant to observe the runtime behavior of the executeTrade() method.

The MCP integration allows the assistant to insert runtime instrumentation without modifying the codebase or restarting the service.

Step 2: Insert a Runtime Log for Trade Execution

To understand what data flows through the method, Lightrun MCP inserts a runtime log at the entry point of executeTrade().

The log captures key values from the trade request and the calculated trade cost.

[TRADE] user={request.userId}
symbol={request.symbol}
qty={request.quantity}
price={currentPrice}
total={totalCost}
highValue={request.highValue}

This log immediately reveals how the trade is being processed under real production traffic.

Step 3: Add a Conditional Snapshot for High-Value Trades

Next, Lightrun MCP inserts a conditional snapshot at the point where the high-value logic executes.

The snapshot triggers only when the trade value exceeds $10,000.

Configuration:

• Condition : totalCost.compareTo(java.math.BigDecimal.valueOf(10000)) > 0

• Max hits: 3
• Target: trade-execution-service-prod

Snapshots capture the complete runtime state, including stack frames and local variables.

Step 4: Observe the Runtime State

When the next high-value trade occurs, the snapshot captures the request object’s runtime state.

request = TradeRequest
symbol = "AAPL"
quantity = 65
price = 425.81
userId = "Philip Jefferson"
highValue = false

Even though the trade value clearly exceeds the threshold, the highValue field remains false. This immediately confirms that the flag assignment failed earlier in the execution path.

Applying the Fix

Once the issue is identified, the fix is straightforward. Instead of relying on reflection, the code should use the setter generated by Lombok.

if (totalCost.compareTo(BigDecimal.valueOf(10000)) > 0) {
    request.setHighValue(true);
}

‘This ensures the correct boolean value is assigned and avoids the fragile reflection-based approach.

Validating the Fix with Runtime Logs

After deploying the fix, Lightrun MCP inserts one additional runtime log before fraud validation.

[FRAUD CHECK] highValue={request.highValue} validating trade for user={request.userId}

The next trade execution confirms that the highValue flag is now correctly propagated to the fraud validation service.

Why Runtime Context Matters

Bugs like this are difficult to detect using traditional observability tools. Metrics and traces can show that requests are processed successfully, but they rarely reveal whether critical flags or internal state values were set correctly during execution.

Runtime context provides a deeper layer of visibility by allowing engineers and AI assistants to inspect the actual behavior of running code. Instead of redeploying services to add instrumentation, teams can dynamically capture the exact data needed to diagnose the issue and verify the fix directly in production.

AI SRE Across the SDLC: MTTR Reduction from Shift-Left to Live Incidents

Sustainable MTTR reduction does not happen only during incidents. It requires reliability practices that span development, deployment, production, and post-incident learning. AI SRE extends beyond reactive response by embedding runtime-verified validation across the entire lifecycle.

1. Shift-Left: Validating Risk Before Deployment

During development, AI SRE systems analyze code changes, deployment patterns, and historical incident data to identify high-risk modifications. When supported by runtime-aware capabilities, AI can validate assumptions against real execution behavior rather than relying solely on static analysis.

Shift-left AI SRE enables teams to:

• Identify risky logic paths before deployment
• Validate conditional branches under realistic inputs
• Detect configuration inconsistencies early
• Reduce production-only edge cases
• Improve release confidence

Reliability becomes embedded in the engineering workflow rather than triggered by a production alert.

2. Live Incidents: Verification Under Real Traffic

During production incidents, AI SRE accelerates triage and reduces manual investigation. When grounded in runtime execution visibility, AI systems can move beyond correlation and directly validate root causes.

Runtime-aware incident workflows enable teams to:

• Correlate alerts across distributed services
• Generate missing runtime evidence on demand
• Inspect execution paths and variable state live
• Validate remediation safely before full rollout
• Shorten MTTR in complex architectures

Instead of relying solely on pattern similarity, teams confirm the exact condition that caused the failure.

3. Continuous Feedback and Reliability Learning

AI SRE also strengthens post-incident workflows by feeding verified runtime evidence back into development and operations. Validated root causes refine detection models, improve alert accuracy, and inform safer architectural decisions. Over time, reliability shifts from reactive firefighting to a continuous, feedback-driven engineering discipline supported by both AI and runtime verification.

Governing AI SRE: Control, Compliance, and Runtime Safety

AI SRE introduces automation into reliability workflows, which makes governance and operational safety essential. In regulated environments such as finance, healthcare, telecommunications, and public sector systems, investigation tooling must operate without altering application state or introducing additional risk. AI-driven reliability must be designed to assist engineers while preserving strict control over production systems.

Enterprise-ready AI SRE platforms must operate under clearly defined safeguards. These safeguards typically include:

• Read-only instrumentation that does not mutate application state
• Strict role-based access controls and approval workflows
• Full audit trails of injected actions and AI-driven recommendations
• Compatibility with on-prem and private cloud deployments
• Minimal performance overhead during runtime inspection

These controls ensure that runtime investigation does not introduce instability or compliance exposure.

Equally important is human oversight. AI SRE should accelerate diagnosis and suggest remediation, but engineers must retain authority over execution. Every action taken by the system should be inspectable, verifiable, and reversible. 

In high-compliance environments, reliability tooling must enhance operational intelligence without compromising governance boundaries. Effective AI SRE balances automation with accountability, enabling faster resolution while preserving enterprise-grade control.

Conclusion

AI has fundamentally changed how software is built. Development velocity has increased, systems have grown more distributed, and failure modes have become more runtime-dependent. As complexity expands, reliability engineering must evolve accordingly. 

Traditional SRE practices rely heavily on predefined telemetry and reactive workflows. Telemetry-driven AI SRE improves signal correlation and automation, but it remains constrained by the limits of what was captured in advance.

Reliable AI SRE requires direct access to runtime execution behavior. Durable MTTR reduction depends on shortening the path from alert to verified execution state. When AI systems can generate missing evidence on demand and validate hypotheses in real time, diagnosis shifts from inference to verification. 

This reduces redeploy loops, shortens MTTR, and increases confidence in remediation decisions. Runtime-aware AI SRE transforms reliability from episodic incident response into a continuous engineering discipline that spans development and production.

The next generation of AI SRE platforms will not rely solely on analyzing telemetry. They will combine intelligent automation with on-demand runtime visibility, enabling teams to investigate safely, validate fixes before impact, and continuously strengthen system resilience. As enterprises adopt AI-driven reliability practices, grounding automation in executional truth will determine which platforms deliver measurable operational improvements.

FAQs

• How can AI SRE reduce MTTR in complex production systems?

AI SRE reduces MTTR (Mean Time To Resolution) by automating alert correlation, identifying probable fault domains, and accelerating root cause analysis. Instead of manually analyzing logs across multiple services, AI models quickly surface patterns and relationships. When combined with runtime-aware capabilities, AI SRE can directly validate root causes, eliminating redeploy loops and shortening investigation cycles.

• How does Lightrun enable runtime-context-aware AI SRE without redeployments?

Lightrun enables AI SRE to generate missing runtime evidence on demand by injecting dynamic logs, snapshots, and metrics into running services. These actions operate in read-only mode and do not require code changes or service restarts. This allows AI-driven workflows to validate hypotheses against live execution behavior without disrupting production systems.

• What makes Lightrun different from traditional telemetry-based AI SRE tools?

Traditional AI SRE tools analyze pre-existing logs, metrics, and traces. Their effectiveness depends on the signals configured before the incident occurred. Lightrun extends AI SRE by enabling direct inspection of runtime behavior, allowing teams to capture missing execution data when needed. This shifts the diagnosis from correlation-based inference to evidence-based verification.

• What are the limitations of telemetry-driven AI SRE in distributed architectures?

In distributed systems, failures often depend on specific runtime conditions, state transitions, or cross-service interactions. Telemetry-driven AI SRE can only analyze data that has already been collected. If the relevant signal was never captured, the AI cannot directly validate the cause. This limitation often leads to redeployment loops, delayed investigations, and increased operational risk.

• How does runtime context improve AI-driven root cause analysis in production?

Runtime context provides visibility into execution paths, variable state, conditional branches, and dependency behavior under live traffic. When AI-driven root cause analysis is grounded in this data, recommendations can be validated directly against real system behavior. This improves remediation accuracy, reduces regressions, and strengthens overall system reliability.

The post How to Reduce MTTR with AI-Powered Runtime Diagnosis appeared first on Lightrun.

NEW YORK, February 25, 2026 — Lightrun, a leader in software reliability, today announced the industry’s first and only real-time AI SRE built on live, in-line runtime context. This allows AI agents and engineering teams to create missing evidence dynamically without redeployments, prove root causes with live execution data (“ground truth”), and validate fixes directly in live environments.

The mass adoption of AI agents and coding assistants has accelerated code generation, outpacing reliability. This has shifted developer time from writing code to verifying and fixing issues, and moved the development bottleneck to runtime, where behavior is complex and often non-deterministic. As enterprises accelerate investment in AI-driven reliability and autonomous operations, this has created a market for AI SREs valued at billions of dollars.

Despite this growth, most available ‘AI SRE’ tools are optimized for post-incident workflows and limited to relying on traditional, static telemetry that was already captured. When logs are missing, traces are incomplete, or execution context is unclear, teams are left to guess. Engineers are forced into long reactive cycles of redeploys, rollbacks, and manual validation. 

Lightrun’s AI SRE closes this gap by bringing live, code-level runtime context directly into the reliability loop. Lightrun has been recognized in the 2026 Gartner® Market Guide for AI Site Reliability Engineering Tooling.

Instead of passively observing telemetry, the Lightrun AI SRE can safely interact with live systems via Lightrun’s patented Sandbox to create new evidence, test hypotheses, and validate outcomes against real execution behavior. This capability transforms AI SRE from a reactive post-incident advisor into a trusted, runtime-verified autonomous engineer that ensures reliability by design.

Built on Lightrun’s Runtime Context engine, the AI SRE supports reliability across the entire SDLC, from proactive issue detection during development and testing (“peace time”) to autonomous investigation and remediation during live incidents (“war time”). It enables teams to understand how code truly behaves in runtime, close visibility gaps without redeploying, and resolve issues with confidence. Lightrun is designed for every team responsible for the behavior, reliability, or outcomes of running software.

“Lightrun addresses a structural visibility gap in the emerging AI site reliability engineering workflows (SRE) market,” said Jim Mercer, Program Vice President, Software Development, DevOps, and DevSecOps at IDC. “By integrating dynamic instrumentation into SRE workflows, the company enables validation of root cause and remediation against live execution, reducing reliance on static, pre-instrumented telemetry and strengthening reliability across the software development lifecycle.”

With Lightrun’s AI SRE, engineering and reliability teams benefit from:

• Root cause analysis based on new evidence from live environments, without requiring prior instrumentation.
• Runtime-validated code changes to eliminate guesswork and reduce rollback-and-redeploy cycles.
• Live issue debugging in safe remote sessions with execution-level behavior inspections.
• Dynamic telemetry to running systems to fill visibility gaps that traditional observability tools cannot address.
• Reduced reliance on expensive war rooms, due to autonomous remediation and the ability to receive a code fix of incidents before escalating to a human.
• Resilience to “unknown unknowns” introduced by multiple AI agents across the SDLC.
  

Zahi Kapeluto, AVP Engineering, AT&T, stated, “Modern, AI-driven software reliability depends on connecting telemetry to real execution context. Without understanding how code behaves in live environments, alerts and metrics alone don’t tell the full story. Lightrun helps our teams close that gap by exposing runtime behavior directly, enabling faster investigation and more confident remediation.”

“AI cannot resolve what it cannot see. Lightrun’s runtime context engine allows AI to see application behavior at a single line level of granularity, which positions us to streamline remediation for any software issues in real-time,” added Ilan Peleg, CEO of Lightrun. “Trusted by Fortune 100 companies and the largest enterprises in the world, Lightrun is proud to lead the way in making self-healing software a reality.”

The post Lightrun Launches Industry’s First AI SRE With Live Dynamic Runtime Context appeared first on Lightrun.

TL;DR

• Runtime context describes the ability to observe how code actually executes in real time, including the precise path a request takes, the state of variables at specific lines, and the conditions under which logic branches evaluate in production.
• Traditional observability systems provide structured telemetry such as logs, metrics, and traces, but they rely on signals defined in advance and therefore cannot always answer newly emerging runtime questions.
• By enabling on-demand inspection of running services, the runtime context allows engineers to retrieve execution-level evidence without rebuilding, redeploying, or waiting for another failure to occur.
• This approach moves engineering workflows from interpreting partial telemetry to validating behavior against live execution, enabling root cause analysis, change validation, live system investigation, and deep code-level analysis.
• As modern systems grow more dynamic and AI participates in development and operational workflows, runtime context becomes a stabilizing layer that aligns diagnosis and remediation with what actually occurred inside the running application.

AI has accelerated the pace at which software can be written, refactored, and deployed. What it has not accelerated is our ability to understand how that software behaves once it runs in production. 

Most critical failures do not emerge in isolation; they arise from real traffic patterns, evolving dependencies, and environment-specific conditions that cannot be fully reproduced in development. Runtime context provides direct visibility into this live behavior, enabling reasoning about software based on observed execution rather than assumptions.

This article explores what runtime context means in practical terms, why traditional observability models leave structural gaps, how root cause analysis changes when execution can be inspected directly, and why runtime context is becoming foundational in AI-driven engineering workflows.

Why is it So Hard to Understand Production Behavior?

Modern systems are distributed, stateful, and constantly evolving. A single request may traverse multiple services, interact with third-party APIs, trigger asynchronous jobs, and depend on cached data whose state has shifted since deployment. Even when individual components are well-tested, their interactions under real-world conditions often introduce behavior that was never anticipated.

Many production incidents emerge from complex interactions across services that are difficult to reproduce outside live environments. The Google Site Reliability Engineering research highlights that distributed systems often fail in ways that cannot be easily simulated in staging environments due to real traffic patterns and evolving dependencies.

When an issue emerges, engineers typically turn to logs, metrics, and traces. These tools are essential for monitoring and alerting, but they are built on prior configuration. They reflect decisions about what to capture at the time of instrumentation. 

If a specific variable, branch condition, or dependency interaction was not logged in advance, it is not available during investigation. At that point, teams often redeploy to add additional logging or attempt to reproduce the issue in staging, where the environment rarely matches production precisely. If they are using an AI tool, it is forced to guess.

This gap between what is happening and what is visible creates uncertainty. Engineers are left to interpret signals rather than observe execution directly. As development velocity increases, especially with AI-generated changes, the number of unanticipated runtime scenarios grows, widening the distance between telemetry and reality.

What Does Traditional Observability Miss?

Observability systems are designed to monitor known signals at scale. Logs capture events that developers anticipated might be relevant. Metrics summarize performance characteristics over time. Traces reconstruct request flows across services. Together, they provide structured insight into system health and are essential for detecting anomalies. However, their strength lies in answering predefined questions rather than exploring new ones.

During incident analysis, the limitation becomes apparent. 

• A spike in latency may be visible in metrics, yet the precise branch condition that triggered it may not have been logged. 
• A trace may show that a dependency call slowed down, but not reveal the data state that caused a retry loop. 
• Logs may confirm that an error occurred, but not capture the variable values that explain why. 

When the required signal is never instrumented, engineers are forced to infer behavior from partial data or initiate redeployment cycles to add instrumentation.

This gap is not a tooling failure; it is a structural constraint. Observability relies on advanced configuration and cost-conscious data collection. It cannot capture every possible runtime permutation without becoming prohibitively expensive and noisy. 

As systems grow more dynamic and AI accelerates code changes, the number of unanticipated execution paths increases. What observability misses, therefore, is not volume, but the specificity, the ability to inspect exactly what happened at the moment the question arises.

What Is Runtime Context?

Runtime context provides execution-level visibility into how code behaves under real-world conditions. It captures what is happening inside an application at a specific line of code, for a specific request, at a specific moment in time. Instead of relying solely on signals emitted in advance, runtime context allows engineers to directly observe execution as it unfolds.

In practical terms, the runtime context makes it possible to inspect:

• The exact execution path taken for a request
• The state of variables at a specific line
• The evaluation of branch conditions
• The behavior of code under real traffic

Unlike traditional telemetry, runtime context is generated on demand and shows live behavior. When a new question arises, why a failure occurred, why a subset of users experienced latency, or why a specific edge case triggered unexpected behavior, engineers can introduce temporary inspection points into the running system without rebuilding or redeploying. 

The investigation can then align to the question rather than be built around prior instrumentation decisions.

How Does Runtime Context Change Root Cause Analysis?

Root cause analysis traditionally begins with interpretation. Engineers and AI SREs review logs, correlate metrics, examine traces, and form hypotheses about what might have happened. 

Each hypothesis must then be validated, often by adding new instrumentation or attempting to reproduce the issue in a controlled environment. This cycle introduces delay and uncertainty because the investigation depends on signals collected before the actual question was known.

Runtime context shifts the investigation model from interpretation to observation, a direct interaction with runtime reality rather than an exercise in deduction.

Instead of inferring behavior from partial telemetry, engineers and AI tools can inspect the exact execution path that produced the failure. They can examine variable values at the moment an exception was thrown, observe how a branch condition evaluated under real data, or understand why a dependency behaved differently for a specific request. 

This has two important consequences. First, hypotheses can be validated immediately against live execution rather than through redeployment cycles. Second, remediation decisions are grounded in evidence rather than probability. Root cause analysis becomes less about narrowing down possibilities and more about confirming what actually occurred.

To put it simply:

• Telemetry helps identify where to look.
• The runtime context shows what happened and explains why.

And that difference changes the speed and confidence of resolution.

Why is Runtime Context Even More Important in the AI Era?

AI has fundamentally changed the pace of software development. Code suggestions, automated refactoring, and AI-generated services have reduced the friction of writing and modifying logic. 

As change velocity increases, the number of possible runtime permutations expands accordingly. More changes introduce more execution paths, more edge cases, and more interactions between components that were never explicitly designed to operate together.

This shift introduces new reliability challenges:

• Rapid code iteration increases behavioral variability across environments.
• AI-generated logic may introduce subtle execution paths that were not manually reviewed.
• Complex service interactions amplify data-dependent and concurrency-driven failures.
• The investigation must now keep pace with the higher deployment frequency.

AI systems themselves depend heavily on available signals. When AI tools analyze telemetry to suggest a root cause or remediation strategy, they are constrained by the completeness of the data provided. If a critical runtime condition was never logged, automated reasoning becomes probabilistic rather than definitive.

Runtime context introduces a necessary grounding layer:

• It exposes live execution behavior rather than relying solely on historical signals.
• It enables AI-assisted workflows to validate hypotheses against real runtime evidence.
• It reduces speculative diagnosis by aligning reasoning with observed execution state.
• It stabilizes reliability in environments where systems evolve continuously.

In an AI-driven engineering landscape, runtime context functions not merely as a debugging enhancement but as an architectural safeguard that ensures both human and automated reasoning remain anchored to execution reality.

How Is Runtime Context Applied in Real Engineering Workflows?

The impact of runtime context becomes clearer when applied to real investigative workflows. 

In practice, it allows engineers to insert temporary inspection points into running services to retrieve execution data precisely when needed. These inspection points can capture logs at a specific line, snapshot variable states during execution, or introduce short-lived metrics to observe behavior over time, all without rebuilding or restarting the application.

This changes how incidents are handled. Instead of rolling back, deploying new logs, and then waiting for another failure, engineers can observe their system live. For example, during an unexpected spike in errors, they can inspect the exact values of the variables that triggered the exception. If latency increases for a subset of users, they can observe the execution branch taken only for those requests. Once the investigation concludes, the temporary inspection logic can be removed, leaving the production environment unchanged.

Platforms such as Lightrun operationalize this model by attaching a production-safe agent to running services and enabling inspection through IDEs, web interfaces, and AI workflows. 

Engineers can interact directly with runtime behavior from the environments they already use, generating evidence on demand rather than relying solely on previously collected telemetry. This allows root cause analysis to move from inference toward confirmation while maintaining production safety.

How is Runtime Context Used Across the SDLC?

Runtime context is often associated with production debugging, but its impact extends beyond incident response. The same capability that enables engineers to inspect a failing request can also be applied earlier in the lifecycle, when assumptions are still being formed, and changes are still being evaluated. Rather than confining runtime visibility to moments of failure, it becomes part of the continuous validation of software.

During Development and Code Changes

When new logic is introduced, whether written manually or generated by AI, the primary risk lies in how it behaves under real-world conditions. Test environments cannot fully reproduce live traffic distributions, data shapes, or dependency states. Runtime context allows engineers to validate behavior against actual execution patterns, reducing the likelihood that subtle edge cases slip into production.

This enables teams to:

• Confirm how specific branches evaluate under real inputs
• Observe performance impact before broad rollout
• Ground AI-generated code suggestions in runtime behavior
• Detect mismatches between expected and actual execution paths

During Incidents and Production Events

When an issue emerges, the runtime context shifts the investigation from interpretation to confirmation. Instead of deploying additional logging or attempting to reproduce the failure, engineers can inspect the exact execution path that triggered it. The same mechanism used during development now accelerates remediation.

This continuity matters. The capability does not change between environments; only the question changes. Whether validating new code or diagnosing an outage, runtime context provides direct access to execution reality.

What Does Runtime Context Look Like in Action?

Consider a trade execution service processing thousands of transactions per minute. 

Here is the scenario:

• Under normal load, trades complete successfully and balances reconcile correctly. 
• However, during peak trading windows, high-value orders intermittently fail with a generic validation error. 
• Infrastructure metrics show no resource saturation, and logs confirm that trades are rejected, but they do not explain why identical orders sometimes pass and sometimes fail under similar conditions.

A simplified portion of the execution logic resembles the following:

if (totalCost.compareTo(BigDecimal.valueOf(10000)) > 0) {
    Field field = TradeRequest.class.getDeclaredField("highValue");
    field.setAccessible(true);
    field.set(request, "true");  // string instead of boolean
}

The logic appears straightforward, yet failures increase for trades above the threshold. Because only the final validation outcome is logged, it is unclear whether the issue stems from incorrect flag assignment, inconsistent fraud validation behavior, or side effects under concurrency.

By using runtime context, the investigation moves from inference to direct observation. A dynamic log inserted at this decision point reveals that the highValue field is not consistently set as expected. Live traffic confirms a type mismatch introduced through reflection, leading to validation inconsistencies that surface only on specific runtime paths.

Further inspection around the account balance validation exposes an additional issue. Concurrent high-value BUY orders read the same balance before withdrawal occurs, creating a race condition during peak load. This behavior does not reproduce reliably in staging because it depends on real concurrency and real timing. 

Telemetry indicated that trades failed, but the runtime context exposed the exact execution conditions responsible for those failures.

How Lightrun Operationalizes Runtime Context Safely

The trading example illustrates the analytical value of runtime context; the remaining challenge is to implement that capability safely in live production systems. In high-stakes environments such as financial platforms, diagnostic tooling must guarantee isolation and non-interference.

Lightrun implements runtime context through a lightweight agent attached to running services. Engineers, AI agents, and code assistants can insert dynamic logs, capture execution snapshots, or introduce temporary metrics directly from their IDE, web interface, or collaboration tools. These inspection points execute immediately against live traffic and can be removed without rebuilding or restarting the application.

In the trading scenario, a dynamic log inserted at the reflection block reveals the actual runtime value and type of the highValue field. A snapshot placed at the account balance validation step captures full variable state, stack trace, and thread context, allowing engineers to confirm concurrency behavior directly under real production load.

All instrumentation runs inside Lightrun’s secure, read-only sandbox, which prevents state mutation and preserves control flow. This ensures that runtime inspection does not alter trade execution or application data. Once the investigation is complete, the temporary instrumentation is removed without residual impact.

Within Lightrun, runtime context appears as line-level execution data attached directly to source code. 

Engineers can observe dynamic logs in real time, inspect snapshots, and correlate findings with existing telemetry. When exposed through the Model Context Protocol (MCP) or Lightrun AI SRE, the same execution evidence becomes accessible to AI-assisted workflows, grounding automated reasoning in live behavior rather than static analysis.By incorporating runtime context into AI SRE workflows, investigations become anchored in verifiable execution evidence, shifting diagnosis from probabilistic inference toward proof-based analysis.

Through this approach, runtime context becomes a practical and production-safe capability embedded directly into investigation and validation workflows.

Conclusion

Software systems are evolving at a pace that widens the gap between rapid code creation and clear understanding of execution. AI has reduced the friction of writing logic, but reasoning about how that logic behaves in distributed, real-world environments remains complex. As change velocity increases, the distance between what is shipped and what can be confidently explained continues to grow.

Observability remains essential, but traditional tools cannot anticipate every execution path or capture every condition that becomes relevant during investigation. Runtime context addresses this limitation by aligning visibility with inquiry, enabling engineers and AI workflows to generate execution-level evidence when new questions arise.

Runtime context is therefore not simply a debugging enhancement but a structural shift in reliability practice across the software lifecycle. By enabling direct observation of live execution, it moves investigations from inference to confirmation while supporting continuous validation of system behavior from development and code changes through incident diagnosis and remediation in production. In AI-driven systems where change is continuous, runtime context becomes a foundational layer for building software that is understandable, trustworthy, and evolved with confidence.

The post What is Runtime Context? A Practical Definition for the AI Era appeared first on Lightrun.

Today, we’re announcing a new MCP integration between Lightrun and Kiro.
Kiro now gains live runtime visibility through the Lightrun MCP, grounding AI-assisted development in how code actually behaves at runtime.

Kiro, the AI coding assistant from the teams at AWS, is built for velocity and intuition. It helps teams move from specification to production faster by turning intent into working code. But until now, like every AI coding assistant, Kiro had a critical blind spot. It could reason about code, but it could not see how that code behaves once it is deployed.

That visibility gap is where many reliability issues begin.

Close the runtime visibility gap with Lightrun MCP

By authorizing the Lightrun MCP in Kiro, you can close the gap. The Lightrun MCP server supplies Kiro with live runtime context, and allows it to reason over real execution data rather than inferences from static information. This context comes directly from running systems and is delivered on demand, without redeploying code or impacting users.

Kiro remains the AI accelerator.
Runtime context is now its source of truth.

Design better code using runtime context

Specifications are fixed. Production is not.

A timeout may look safe and a retry strategy reasonable until real traffic proves otherwise. As AI-assisted development accelerates, teams are often forced to assume generated code will behave correctly under live conditions.

Runtime context removes the need to assume.

With Lightrun MCP, Kiro can inspect live execution paths, observe how data actually flows through services, and see which conditions occur in real environments. As new code is designed, Kiro can reasons over real system behavior and architecture, basing its decisions in runtime evidence rather than theoretical models.

This shifts reliability into the design phase, helping teams build code that reflects how their systems actually behave.

Investigating issues using runtime evidence

Until now, AI coding assistants have not been able to see what happens once code leaves the IDE.

When systems fail or behave unusually, engineers are forced into manual investigations, often of unfamiliar code. The scan logs, switch between tools, and add instrumentation just to confirm a hypothesis.

This is often where teams lose much of the time that AI assistance initially saved.

With Lightrun MCP, investigation no longer starts with guesswork. Kiro can independently reason over live runtime context to observe variable values at failure points, confirm which execution paths were taken, and verify hypotheses about root causes against real system behavior.

Engineering teams stop guessing and start seeing what is actually happening. Feedback loops shorten without adding operational overhead or requiring redeploys.

Validate changes before they impact users

The riskiest moment in development is merging a change that behaves differently under live conditions and impacts users.

With runtime context available, Kiro helps validate changes against live, sandboxed execution behavior before users are affected. Fixes suggested by the assistant or the engineer can be evaluated based on whether they stabilize execution paths and perform as expected under real traffic.

Validation becomes evidence-backed rather than assumptive, reducing the number of surprise regressions, allowing teams to trust their AI-assisted code running in production.

How to set up Lightrun MCP in Kiro?

To give an AI coding assistant like Kiro access to runtime context, three components are required.

1. A running application
   Any service where Lightrun is already attached or can be attached, in staging or production-like environments.
2. The Lightrun MCP server enabled
   This acts as the bridge between your running code and the AI assistant, exposing live runtime context in a safe, controlled way.
3. An MCP-enabled AI client
   Once connected, your AI assistant can query runtime context such as variable values, execution paths, and call stacks, without redeploying or changing code.

Once these are in place, Kiro can access live runtime context without altering the code or requiring redeployments.

Get started today

The Lightrun MCP integration with Kiro is available now.

For AI to let your teams move fast, it needs evidence.

• Learn more about how to set up the Lightrun MCP server.
• Quickstart with setting up the MCP.

The post Kiro Can Now Use Lightrun via MCP appeared first on Lightrun.

TLDR: AI coding assistants have sped up code delivery, but created a validation gap. Historic telemetry and static analysis cannot predict the behavior of unfamiliar, high-volume code. Runtime Context MCP bridges this gap, allowing AI assistants to verify behavior before it breaks, and resolve issues live.

Why is AI-generated code failing in production?

The advent of AI assistants like Cursor, Claude Code, and GitHub Copilot have reset expectations for how quickly teams can ship software. Cursor reports that engineers have increased PR merges by 39%. However, this velocity has a significant cost to system reliability.

These AI tools operate primarily with static context. They can understand what code looks like and can review data from historic logs and codebases, but they are blind once it leaves the IDE. As Google Cloud’s 2025 DORA report demonstrated, AI adoption is coinciding with an almost 10% increase in delivery instability. 

What is Runtime Context for AI agents?

In the AI era, ‘context’ usually refers to the static data passed into a prompt (source code, documentation, or history). Runtime Context is fundamentally different: it is the live, execution-level state of a running application (variables, call stacks, logs, and metrics) available to an AI during its reasoning loop.

If the LLM is the brain, Runtime Context is the nervous system. It allows an agent to move from probabilistic guessing (writing code that should work based) to deterministic validation (verifying that code actually works in the current environment).

How does the high code volume impact stability?

As AI-assisted coding accelerates, teams are merging high volumes of unfamiliar code into complex systems. Reviewing this code through traditional PR cycles is becoming an exercise in approximation.

When change volume outpaces our ability to verify it fully, we create a stability debt.

We are shipping code faster than we can understand its impact. Without a way to verify these changes against live state, the speed gained during development is lost to lengthy incident-resolution loops.

Three levels of AI code assistant awareness 

To build stable systems in high-volume environments, AI assistants need more than just a view of the repository; they require a tiered understanding of reality created from three levels:

1. Local context: This is visibility of the immediate file, ideal for syntax, logic, and local refactoring, but it’s blind to dependencies and system architecture.
2. Global context: This is awareness of the entire repository, enabling architectural consistency and multi-file logic. However, it’s static, reflecting what the code is, not how it behaves
3. Runtime context: This is clarity about the live running application. It provides the ground truth of a system, so code can be validated against real traffic and data.

Currently, most AI assistants rely on the first two.

They can reason about theoretical correctness, but they cannot guarantee stability under load because they cannot access Runtime Context into how the system is behaving live, without first redeploying.

Why do AI assistants “hallucinate” environments?

When an agent lacks Runtime Context, AI assistants need to infer environmental conditions. It assumes database indexes exist, services respond instantly, and data shapes match contemporary documentation.

The result is an AI-generated code change that looks correct but which triggers failures the moment it interacts with real-world variables. This is not a failure of AI reasoning; it is the absence of ground truth. Without a view into runtime reality, assistants cannot explain why a fix failed, or provide a reliable alternative, they can only hallucinate.

Verifying behavior with the MCP workflow

Bridging this gap requires a fundamental shift in how AI interacts with live systems. This is the core value of Lightrun’s Runtime Context MCP. It moves the AI from reactive troubleshooting to proactive verification.

Instead of waiting for an incident, the AI agent can the AI agent can interrogate the live service directly through the IDE using the Model Context Protocol (MCP). Engineers can verify the application’s runtime behavior directly through natural language prompts. The investigation happens inside the IDE, using the same conversational interface as the assistant, without switching tools.

1. Create on-demand ground truth 

• The AI interrogates the live service to validate its assumptions.
• It verifies data shapes, check real-world latency, or capture snapshots to move from hypotheses to evidence. 

2. Verify conditional logic in production

• The AI investigates paths that only trigger under specific states (e.g., a transaction exceeding $1,000).
• It injects dynamic logs specifically where the logic branches to ensure edge cases are handled.

3. Ensure cross-environment parity

• A code change might work locally but fail in Production due to configuration drift or data volume.
• Behavior is also impacted by interactions with third-party services.
• AI assistants use the MCP to validate behavior across environments to ensure the fix holds.

4. Save engineer time with the zero-redeploy loop 

Traditional observability requires a code change, rebuild, and redeploy to add a single log line. The MCP workflow bypasses this:

• Observe: The AI securely queries live applications using sandboxed investigations.
• Validate: It pulls state directly from the running system to confirm correct functionality.
• Fix: The AI proposes a solution based on live evidence, not a guess.

Runtime Context: The key for reliable AI-assisted engineering

As we rely more and more on AI assistants to generate our code, we must give them the tools to ensure reliability in execution.

The most reliable code is not just elegant, it is validated against real traffic and real failure conditions.

The ability for assistants to verify runtime behavior, identify weakness, and suggest fixes, without redeploying, is the new gold standard for AI-accelerated engineering. Lightrun’s MCP makes this possible by giving AI assistants a secure, on-demand way to interrogate live systems. Not to observe passively, but to validate assumptions, test hypotheses, and prove behavior without redeploying.

If we trust AI to write our code, we must give it the eyes to verify it. Runtime context is that proof.

The post How to Make AI-Generated Code Reliable with Runtime Context appeared first on Lightrun.

AI assistants can generate code rapidly, but studies from Stanford and Google have shown that it fails at high rates once exposed to real-world traffic, dependencies, and workloads. Furthermore, once the code leaves the Integrated Development Environment (IDE), AI cannot see what takes place in staging, pre-production, or production. As a result, teams report spending up to 17 hours a week debugging and refactoring bad code and 60–70% of teams’ time spent debugging and a 41% rise in bug rates.

Lightrun’s Runtime Context directly addresses this problem by bridging the gap between the IDE, the AI assistant, and runtime, providing crucial context to the agent and the developer behind it. Developers can now ask their coding assistant to check environment traffic before writing a module, investigate a production failure, or add the instrumentation needed to validate behavior. Lightrun’s MCP acts as the secure bridge, enabling the AI agents to add logs and traces in real time, capture snapshots, investigate issues safely, and even suggest fixes, all without requiring engineers to manually reproduce issues. Runtime context enriches every AI-generated line of code with inline runtime context and observability. This extends across the SDLC and into production, helping engineers move faster while ensuring code remains reliable under real-world conditions

“AI has taken over much of the creative part of coding,” said Ilan Peleg, CEO and co-founder of Lightrun. “However, debugging across environments has remained painfully manual. With Runtime Context, AI can finally participate in the full lifecycle by writing code, validating and debugging it, and remediating issues based on real-world behavior. This is the next evolution of autonomous software development.”

The Runtime Context model enables AI agents to:

• Trigger remote debugging sessions inside staging, pre-production, or production
• Access production-grade telemetry in real time
• Propose fixes based on actual runtime behavior
• Deliver code that is reliable, stable, and deployment-ready

Lightrun customers can now expect faster debugging cycles, higher deployment reliability, and AI-generated code that better withstands real traffic and dependencies.

The post Lightrun ‘Runtime Context’ Empowers AI Coding Agents to Build Software That Works in the Real World appeared first on Lightrun.

You add logs, you capture data, you redeploy, and suddenly your browser is full of open tabs, copied JSON blobs, and screenshots of log lines. Comparing behavior between two requests, two users, or two releases turns into a manual, error-prone chore.

Lightrun Snapshots were built to fix the data collection side of that story. Now we’re tackling the comparison side too.

In this post, we’ll walk through a new way to compare variable values across snapshot hits directly inside Lightrun so you can reason about your application’s behavior without juggling terminals, editors, and scratchpads.

Why comparing snapshot hits matters

Lightrun Snapshots let you capture the full local state at a line of code in a running application, without redeploying and without polluting your logs. That’s powerful on its own, but most real debugging scenarios involve at least two data points:

• A “good” request vs. a “bad” one
• Behavior before and after a recent change
• A snapshot from staging versus one from production
• A single user’s failing flow compared to a baseline user

In each of these cases, you’re trying to answer a simple question:

“What’s different in the local state between these runs?”

Until now, that gap was often filled with copy-paste: copying JSON payloads into a note, flipping between windows, or dumping values into a spreadsheet.

The new comparison capabilities are designed to bring that whole process into Lightrun.

Two ways to compare snapshot data

The experience centers on two complementary workflows:

1. Compare snapshot hit variable values in the clipboard
2. Compare variables across snapshot hits

You can think of them as:

• Clipboard comparison → quickly eyeball differences between a handful of values
• Cross-hit comparison → systematically compare variables across multiple snapshot hits

1. Compare snapshot hit variable values in the clipboard

Sometimes you don’t need a full analysis. You just want to grab a few values from one or more snapshot hits and answer: “Are these the same or not?”

The clipboard comparison flow is built for that.

How it works

Capture your snapshot hits

Use Lightrun Snapshots as you already do: set conditions, trigger events, and let hits accumulate for the code location you care about.

Select the variables you care about

From a given snapshot hit, pick one or more variables (or nested fields) that are relevant to your investigation: IDs, flags, configuration values, response payload fields, and so on.

Send them to the comparison clipboard

Instead of copying these values into an external note, add them to Lightrun’s internal comparison clipboard. You can repeat this across different hits.

View differences side by side

In the clipboard comparison view, those values are lined up so you can visually spot what changed between hits.

Why it’s useful

• Less friction, less context switching

You no longer have to copy JSON into a text editor just to compare two fields. The comparison happens where you collected the data.

• Focus on what matters

You decide which variables matter for this bug. The clipboard is a curated set of values, not the entire snapshot payload.

• Great for quick hypotheses

If you suspect a single field (like featureFlagEnabled or tenantId) is driving the bug, clipboard comparison confirms or rules that out in a few seconds.

This feature is available across all IDEs in Lightrun 1.70+.

2. Compare variables across snapshot hits

For deeper investigations, you often need to look beyond a few fields. You want to treat multiple snapshot hits as rows in a table and compare many variables as columns.

That’s where cross snapshot-hit variable comparison comes in.

How it works

1. Choose the snapshot hits to compare

From your list of hits, select the ones that represent the cases you care about, for example several failing hits, a couple of successful ones, or hits from different environments.

1. Select variables across those hits

Pick which variables should be part of the comparison: function arguments, derived values, config objects, user context, and more.

1. See a structured comparison view

Lightrun presents the selected variables across the selected hits in a structured format so you can quickly scan:

• Which values are identical across all hits
• Which values differ only on failing hits
• Which values change consistently between environments or releases

Why it’s powerful

• Turn snapshot hits into a mini dataset

Instead of skimming one hit at a time, you get a small table of runs vs. variables that surfaces patterns.

• Ideal for regression and environment mismatch debugging

If a bug appears only in production, you can compare production snapshot hits with their staging equivalents and immediately see configuration or input differences.

• Helps you confirm causality

When you see that a single variable is consistently flipped or diverging only in failing hits, you can move from “this might be related” to “this is very likely the cause.”

The cross-hit variable comparison feature is now available for the JetBrains IDE with Lightrun 1.72+.

Example scenarios where this shines

Here are a few real-world flows where these comparison features save time.

1. “It only fails for some users”

• Capture snapshots on the failing endpoint.
• Collect hits for users where the request fails and where it succeeds.
• Use cross-hit comparison to look at user context, feature flags, or tenant configs.
• Quickly see which flag or field differentiates the broken users from the healthy ones.

2. “It works in staging but not in production”

• Place a snapshot at the suspicious line of code.
• Trigger it in staging and in production with similar input.
• Compare environment variables, configuration objects, or external API responses across hits.
• Let the comparison view highlight which value diverges between environments.

3. “This started after the last release”

• Capture snapshot hits before and after your deploy (or across two snapshot configurations).
• Compare arguments, internal state, and derived values across versions.
• See what behavior changed, even if you never logged those values before.

Designed for developers, not dashboards

These comparison capabilities are built for a developer’s day-to-day workflow:

• Zero redeploys

Like all Lightrun Snapshots, you can add and remove these comparisons dynamically in a running system.

• No code pollution

You’re not adding temporary logs and TODOs just to inspect state. You’re using the snapshot data you already collect.

• Fast iteration

As your mental model of the bug evolves, you can add or remove variables from the comparison and focus on the ones that look most suspicious.

Getting started

If you’re already using Lightrun Snapshots:

1. Capture a few snapshot hits on a line that’s involved in a known tricky issue.
2. Send some variable values to the hits table to explore cross-hit comparisons.
3. For a more in-depth investigation, copy a value from one snapshot to the clipboard and then compare with the same value in another.

If you’re new to Lightrun, this feature is a good illustration of what you gain from production-safe, on-the-fly observability: not just more data, but better tools to understand that data.

Wrap-up

Debugging is ultimately about understanding how and why your application behaves differently across requests, users, environments, and releases. Snapshots answer the “show me what’s happening right now” part. With variable comparison in the clipboard and across snapshot hits, Lightrun now helps you answer “what’s different between these runs?” just as effectively.

If you’re ready to stop copy-pasting JSON into notes just to compare two values, this feature is for you.

Want to see it in action?

Log into your Lightrun customer space and start comparing snapshot hits, or reach out to our team for a quick walkthrough tailored to your stack.

The post Side-by-Side Variable Comparison for Snapshot Debugging appeared first on Lightrun.

Every hour developers spend troubleshooting is an hour they’re not building features, innovating, or delivering value to customers. Yet in most organizations, issue management and debugging remains one of the biggest drains on productivity and release velocity. That frustration is exactly what led our founders, themselves developers, to create Lightrun.

Research shows developers spend 25–50% of their time debugging. That means millions of dollars in lost opportunity, delayed releases, and lower developer satisfaction. And this is a concern that’s only getting magnified as generative AI gives us more code and more dependencies than ever before. Stanford’s 2025 study involving 100k developers concluded that 15-25% of the productivity gain from gen AI was lost again on rework due to bugs.

Below are the four most common inefficiencies in debugging, what they mean for your business, and how Lightrun helps fix them.

1. Issues Are Hard (or Impossible) to Reproduce Locally

The status quo

Modern large, distributed systems can’t be fully replicated on a developer’s laptop (see research by Microsoft as well as a white paper from Google). The differences in dependencies, such as use of 3rd party API calls, may make reproduction in lower environments impossible. Some bugs are also due to matters of scale, such as the number of instances of backend services running in tandem. Remote debuggers exist, of course, but successfully using them to target the right service instance can be a real challenge, and these tools bring performance risk as well as security concerns if used in user-facing environments. And let’s not forget about the elephant in the room: real-world data. Many bugs only manifest once end users bring their unexpected data and usage circumstances into the equation.

41% [of engineers] identified reproducing a bug as the biggest barrier to finding and fixing bugs faster. Undo.io report

The impact

The time to resolve an issue (MTTR) goes up and up when the root cause of that issue isn’t easy to identify. Which means that teams managing services run the risk of violating service level agreements with customers (SLAs) or internal objectives (SLOs), angering customers and doing reputational harm. Internally, the situation leads to frustrated teams and lower morale because, let’s face it, engineers don’t enjoy spending time in war rooms.

2. Redeployments are Slow and Inefficient

The status quo

Debugging often means adding logs, redeploying, testing, and repeating. Each of these cycles is slow, and developers lose even more time context-shifting while waiting for new deployments. In some organizations, to even initiate these cycles, developers often need approved change requests and Ops/SRE team involvement to redeploy builds with added logging. Multiple teams are thus pulled into incident response, slowing resolution.

31% of the time, developers have to ask for more logs from the reporter. IEEE Study

The impact

Naturally, incident response time suffers due to these cycles, prolonging service interruptions. Not to mention lost developer time and delayed feature releases because developers are focused elsewhere. Developer morale can plummet as their time is further wasted waiting for access to logs from environments they can’t interact with directly. Operational costs go up as teams spend hours on repetitive work.

3. Too Much Telemetry Complicates RCA

The status quo

Due to the inefficiencies already mentioned above and to avoid missing root causes, teams often over-log their applications to try and game the system around the pain of redeployment. As a result, the logs that Ops/SREs are forced to contend with can wind up being incredibly noisy, creating a needle-in-a-haystack problem when searching for the root cause of a specific problem. This is such a well-known problem that there’s even a CWE for it, as well as published guidance from Google, Microsoft, and Amazon to try and avoid this anti-pattern.

The impact

Engineers waste time searching through irrelevant data. Again, incident response time suffers. Not to mention that this glut of logging can cause storage & retention costs to skyrocket.

4. Troubleshooting Unfamiliar Code Is Hard

The status quo

Developers are often faced with troubleshooting issues in code they didn’t write. The classic flavor of this was due to code written by people no longer on the team. These days everyone is facing it because so much of the code was written by AI. Which means devs spend hours building context before they can even start debugging.

…if you didn’t write the code, and you didn’t struggle with the code, you simply aren’t going to be able to just sit down in front of that code and ‘get it’ immediately. I don’t care how smart you are. It’s not about ‘smart.’ It’s about being in the trenches during the original battle. Joe Procoprio writing for Inc

The impact

This particularly inefficiency can lead to longer onboarding cycles for new developers. They might not be trusted to handle high priority issues due to lack of context, which means senior developers are assigned instead and now your best peoples’ time is getting soaked up by troubleshooting. Do we even need to mention the negative impact on incident response time again? There’s also increased risk of repeat incidents due to incomplete fixes.

How Lightrun Addresses these Problems

Lightrun enables safe, live debugging in any environment, including production. With patented Sandbox technology and flexible data redaction, developers can observe exactly what’s happening without impacting performance or security.

With Lightrun, developers can insert dynamic logs or snapshots (virtual, non-breaking breakpoints) directly into running applications, with no redeploy required. This reduces MTTR by up to 90% and accelerates time-to-resolution.

Lightrun lets developers self-serve this instrumentation directly from their IDE. This reduces dependency on Ops/SRE, shortens resolution cycles, and frees every team to focus on higher-value work.

Lightrun’s AI-powered Autonomous Debugger helps developers quickly orient themselves with root cause analysis assistance and guided instrumentation suggestions. This shortens onboarding time for new devs, accelerates troubleshooting, and improves long-term team resilience.

Conclusion

Debugging inefficiencies are more than engineering headaches—they’re direct drains on velocity, costs, and developer retention.

With Lightrun, organizations achieve:

• Up to 90% faster MTTR
• Hundreds of thousands in annual incident savings (PPG saved $322K)
• Lower observability costs through optimized logging
• Faster onboarding and happier developers

For engineering leaders, that means shipping more features, achieving your quarterly goals, and giving developers time to focus on innovation.

Ready to turn debugging from a headache to a delivery enabler? Book a demo today.

The post Top 4 Inefficiencies For Dev Teams Resolving Issues appeared first on Lightrun.