Inspiration

I've spent countless nights on-call, staring at dashboards that were blinking red but telling me absolutely nothing. In the world of microservices, we've become experts at collecting data but terrible at understanding it. We drown in logs, traces, and metrics.

My inspiration for GreenStick came from a simple question:

Why am I the one connecting the dots?
Why do I have to manually correlate a database latency spike with a frontend 500 error?

I wanted to build an agent that didn't just monitor the chaos, but actually understood the structure of it. With the release of Elastic's ES|QL, I finally had the tool to make that possible.


What it does

GreenStick is an autonomous reliability agent that acts as a “Master Builder” for your cloud infrastructure. Instead of passive monitoring, it actively investigates incidents using a tool-use architecture.

Key Capabilities

  • Cascading Failure Detection
    Uses ES|QL aggregations to instantly identify patient zero in a microservice chain reaction.

  • Autonomous Investigation
    When an alert fires, GreenStick wakes up, runs a suite of diagnostic tools (Trace Analysis, Error Trending, Anomaly Detection), and synthesizes a root-cause report.

  • One-Click Remediation
    It doesn’t just find the problem — it offers precise actions like restarting a specific service pod or rolling back a deployment.

It turns mean time to innocence from hours into seconds.


How I built it

I built GreenStick as a bridge between modern LLM reasoning and hard data analytics.

The Brain — Gemini 2.5 Pro

Google’s Gemini model acts as the cognitive engine. It parses natural-language intent and decides which analytical tools to run.

The Engine — Elastic ES|QL

This was the game-changer. I replaced hundreds of lines of Python data processing with elegant, piped ES|QL queries.

Example:

STATS error_count = COUNT(*) BY trace_id
| WHERE distinct_services > 1

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