I help organizations ship governed AI systems without the compliance debt that derails most AI initiatives. Federal or commercial. Now builds, deploys, and governs AI agents end to end.
Meridian Live — Control plane for governed RAG systems. Deterministic retrieval · Explicit refusal semantics · Citation validation · Structured telemetry
Prevents the compliance failures and audit gaps that surface when RAG systems are deployed without governance.
Validated through real-world agent workflows, including failure diagnosis and controlled execution under production-like conditions.
AI systems fail from architectural ambiguity, not model weakness.
AgentBond — Capability-based enforcement layer for agent delegation and tool access.
Issues scoped, non-redelegable tokens that bind:
- allowed tools
- resource boundaries
- time constraints (TTL)
Every action is validated at execution time: signature · scope · policy · audit
Prevents confused-deputy problems and limits blast radius even under orchestrator compromise.
Forms the hard trust boundary between agent intent and system execution.
aiPolaris (Feature Product) - Regulated AI agent orchestration stalls on compliance gaps, audit failures, and capability boundaries that aren't enforced until production. aiPolaris prevents that — from the first commit.
AI systems fail from architectural ambiguity, not model weakness.
Next: Enterprise Agentic RAG — LangGraph multi-agent system with Graph API, ADLS Gen2, Azure AI Search, Entra ID auth, and GCCH-scoped Terraform. Built to demonstrate the full delivery process from intake to ATO-ready release records.
Dead Letter Oracle — MCP-based agent system for diagnosing and safely replaying failed messages with governed execution and audit traceability.
Regulated AI initiatives stall because there is no delivery process — not because the engineering is wrong. Every engagement runs the same five-phase loop: intake, parallel execution, integration, delivery, and continuous operations. The compliance evidence accumulates as the system is built, not after.
| Role mode | What it produces |
|---|---|
| Business Analyst | Use cases, system boundary doc, acceptance criteria |
| ML / AI Engineer | Agent graph, eval harness, prompt versioning |
| Data Engineer | Graph API connectors, ADLS pipeline, AI Search index |
| DevOps / MLOps | Terraform (commercial + GCCH), CI/CD, release records |
| Security | Threat model, NIST control mapping, SAST gates |
| Domain | Technologies |
|---|---|
| AI Orchestration | LangGraph, Semantic Kernel, AutoGen, MCP tool servers |
| Retrieval | Azure AI Search, pgvector, Chroma, RAG pipelines |
| LLMs | Azure OpenAI, Claude (Opus/Sonnet), Ollama (local) |
| Data | Graph API, ADLS Gen2, Azure Data Factory, Kafka, NATS |
| Backend | Python, FastAPI, C#/.NET Core, TypeScript, gRPC |
| Cloud & Infra | Azure (GCCH-ready), AWS, Kubernetes, Terraform, AKS |
| Compliance | NIST 800-53, FedRAMP, ATO-ready, active secret clearance |
- Anthropic: Developing Applications with Claude API
- Anthropic: Mastering Claude AI — Prompting, APIs, RAG, and MCP
- Anthropic: Claude Code in Action · Introduction to MCP
- AWS: Generative AI & AI Agents with Amazon Bedrock
- AWS: Security Governance at Scale
- Microsoft: Azure Cognitive Services
- Microsoft: AI agent fundamentals with Azure AI Foundry
Control precedes generation. Observability precedes scale. Governance precedes automation.
I design systems where failure modes are explicit, validated, and controlled before production.
- DLQ failure diagnosis and governed replay
- Schema mismatch detection with validation loops
- Controlled tool execution via MCP enforcement boundary
- Agent decision traceability with audit reconstruction