Agentic AI Orchestration
The question is no longer whether to adopt AI - it is whether you can move AI from pilot to production at scale. I build engineering organizations where agentic AI is not a tool bolted on top of the SDLC, but a core operating layer embedded throughout it.
My approach delivers measurable outcomes: 55% reduction in developer toil, 70% of routine PR reviews handled by AI agents, and 4x faster developer onboarding through AI-assisted context delivery. I implement using production-grade frameworks - from Claude-powered architecture reviews to multi-agent orchestration via MCP (Model Context Protocol) - to drive operational excellence, not experiments.
I build the governance layer too. Agentic AI in 2026 requires defined reliability standards, agent audit trails, cost accountability (FinOps for agents), and clear human-in-the-loop policies. Teams I lead ship with AI confidence - not AI chaos.
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What I Deliver
Multi-Agent Systems
I design and implement multi-agent orchestration architectures where specialized agents collaborate on complex engineering workflows - from intelligent code review to autonomous incident response. Built for production reliability, not demo success.
MCP Implementation
Model Context Protocol (MCP) is the standard for connecting AI agents to enterprise tooling. I implement MCP servers that give agents secure, governed access to codebases, CI/CD systems, databases, and internal APIs - turning AI assistants into active operators.
AI Governance & FinOps
Production-grade agentic AI requires accountability frameworks: agent audit trails, reliability SLOs, cost-per-agent tracking, and human-in-the-loop policies. I build the governance layer that gives CFOs and boards the confidence to scale AI investment.
Measurable AI Outcomes
Real results from production agentic AI deployments - not benchmarks from AI marketing decks.
Reduction in Developer Toil
Routine tasks - PR triage, test generation, documentation, dependency updates - handled by AI agents that operate continuously and consistently.
PR Reviews AI-Assisted
70% of routine pull request reviews - style, security, test coverage, dependency checks - handled by AI agents before a human reviewer sees the diff.
Faster Developer Onboarding
AI-assisted context delivery - codebase walkthroughs, architecture Q&A, runbook automation - reduces new engineer time-to-first-contribution from weeks to days.
How I Work
- Assess current AI maturity
I audit existing AI tooling, identify automation gaps, and map where agents can replace human toil with zero quality loss.
- Design the agent architecture
I define the agent graph, tool access via MCP, orchestration patterns (A2A), and reliability requirements before any code is written.
- Implement with production standards
Agents are built with the same engineering rigor as production software: testing, observability, rollback plans, and SLOs.
- Build the governance layer
Audit trails, human-in-the-loop checkpoints, FinOps dashboards, and agent reliability reporting - so you can scale AI with confidence.
Real-World Agent Use Cases
Code Review Agents
AI agents that review pull requests for style violations, security vulnerabilities, test coverage gaps, and dependency issues - before a human reviewer sees the diff. 70% of routine reviews handled autonomously, freeing senior engineers for high-value feedback.
Incident Response Agents
Agents that triage production incidents, correlate logs and metrics, identify probable root causes, and escalate with full diagnostic context - reducing mean time to resolution (MTTR) and alert fatigue for on-call engineers.
Developer Onboarding Agents
AI-assisted onboarding that delivers codebase walkthroughs, architecture explanations, runbook automation, and Q&A on demand - compressing new engineer ramp time from weeks to days and freeing senior engineers from repetitive onboarding tasks.
Agent Technology Stack
I select agent frameworks based on what delivers production reliability - not what is trending on social media. The stack I build with is proven in real engineering environments.
- Claude + MCP
Claude for reasoning and code-aware tasks, MCP (Model Context Protocol) for governed tool access. The standard for connecting agents to enterprise tooling securely.
- A2A Protocol
Agent-to-Agent (A2A) protocol for orchestrating multi-agent workflows where specialized agents collaborate on complex engineering tasks.
- LangChain, LangGraph & CrewAI
Orchestration frameworks for complex agent pipelines - LangGraph for stateful workflows, CrewAI for role-based multi-agent collaboration, LangChain for flexible tool integration.