Agentic AI Orchestration
The question is no longer whether to adopt AI - it is whether you can move it 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: 5x deploy frequency, 23% PR throughput gain, test coverage lifted from under 10% to 40% with no dedicated QA team, and new-engineer onboarding cut 70%. I run a multi-model strategy across Claude Code, GitHub Copilot, AWS Kiro, OpenAI Codex, Gemini, Bolt, Lovable, and Snowflake Cortex, with frontier open-weight models on vLLM for greenfield agentic platforms - so teams develop fluency across providers with no single-vendor dependency. I have partnered directly with CPOs on Lovable and jointly with product teams on Bolt - extending AI tooling beyond engineering into rapid product prototyping that engineering then hardens.
I build the governance layer too. Agentic AI in 2026 requires defined reliability standards, agent audit trails, cost accountability (FinOps for AI), and clear human-in-the-loop policies. Teams I lead ship with AI confidence - not AI chaos.
Hire Me
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.
Deploy Frequency
AI-native SDLC running Claude Code, GitHub Copilot, and AWS Kiro as first-class GitHub Actions pipeline stages - code generation, test synthesis, cloud architecture scaffolding. Code-to-release cycle time down 40%.
PR Throughput Gain
AI-assisted code review, test generation, and documentation refresh shipped throughput improvements without sacrificing quality. SonarQube enforces project standards in CI/CD as a hard gate.
Onboarding Time Cut
AI-assisted documentation refresh plus a lead-mentor program compressed new-engineer time-to-first-commit by 70%. Test coverage lifted from under 10% to 40% with no dedicated QA team.
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.
Where AI Fits - and Where It Doesn't
The most valuable thing I do for a company is not writing the code itself. It is the judgment call on where AI earns its keep and where it actively makes things worse. That call is rock-solid because I have been writing software for two decades and leading engineers for fifteen years - I know what every frontier model is good at, what it is not, and how to keep teams shipping without losing their judgment to it.
I am not a 40-hour-a-week individual contributor. I review pull requests, run architecture reviews, and challenge senior engineers on design decisions. The coding I do myself is mostly to identify and automate the repetitive work across the company - not just my own inbox. One-time scripts, custom MCP servers, or no-code and low-code automation on Claude CoWork, Computer Use, OpenAI Codex, or Zapier - whatever creates the force multiplier for that team.
I stay current on Claude, OpenAI Codex, Gemini, Kiro, Bolt, Lovable, and frontier open-weight models constantly - evaluations, benchmarks, and real-workload tests - so the multi-model strategy I recommend is grounded in current evidence, not last quarter's hype.
When I push for AI
- Repetitive, well-specified work (PR triage, test generation, doc refresh)
- Rapid prototyping with CPOs and product teams (Bolt, Lovable)
- Coverage gaps in QA and observability
- Onboarding context delivery for new engineers
When I push back
- Irreversible production decisions without human review
- Compliance-sensitive workflows without audit trails
- Cost models that are not actually tracked (FinOps theater)
- "Replace the QA team with AI" pitches that skip the judgment
AI-Native Developer Experience
Agentic AI is only useful if it shows up in the metrics that matter. Below are the DevEx outcomes from operationalizing AI across a growth-stage engineering organization.
5x Deploy Frequency
Claude Code, GitHub Copilot, OpenAI Codex, and AWS Kiro running as first-class CI/CD stages. Code-to-release cycle time down 40%.
Ship Fast, Ship Quality
AI-generated unit tests plus Playwright-driven UI regression suites lifted coverage from under 10% to 40% with no dedicated QA team. Cross-trained Product on Playwright for smoke and release-regression tests. Speed and quality, not one or the other.
70% Onboarding Cut
AI-assisted documentation refresh plus a lead-mentor program. Measurable time-to-first-commit improvement on every new hire.
Velocity Metrics That Matter
MTTR, deploy frequency, PR throughput, SLA attainment, onboarding speed - tracked, trended, tied to engineering investments.
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, and I run it as a multi-model strategy so teams develop fluency across providers with no single-vendor dependency.
- Claude Code + GitHub Copilot + AWS Kiro
Frontier coding agents running as first-class GitHub Actions pipeline stages - code generation, test synthesis, cloud architecture scaffolding, and documentation refresh. Early-adopter design partner for AWS Kiro (Amazon Q replacement), bringing pre-GA tooling into the platform roadmap.
- OpenAI Codex, Gemini, Bolt, Lovable & Snowflake Cortex
OpenAI Codex for prototyping and code review, Gemini for long-context reasoning, Bolt and Lovable for rapid app scaffolding (used jointly with CPO and product teams to extend AI beyond engineering), Snowflake Cortex for warehouse-native analytics. Multi-model fluency tracked as a leading indicator alongside delivery and quality metrics.
- vLLM + Frontier Open-Weight Models
Greenfield agentic supply chain intelligence platform on Python FastAPI and vLLM with Qwen3.6-27B-class open-weight models, combining contract performance with utilization analytics. Multi-engine LLM serving with runtime model swap. Production patterns: two-pass contract extraction with per-field confidence scoring (auto-flag below 70% for human review), AWS Textract OCR fallback, fuzzy plus semantic vendor matching, and a compliance scoring engine producing Critical/High/Medium/Low risk classification from committed-vs-actual spend, run-rate projection, standardization share, and penalty exposure. Late-stage architectural consolidation from a hybrid Java + Python stack to Python-only removed an unnecessary service hop. Read the full case study →
- MCP, A2A & Agent Frameworks
MCP (Model Context Protocol) for governed tool access, A2A for agent-to-agent orchestration, LangGraph for stateful workflows, CrewAI for role-based multi-agent collaboration. The connective tissue between models and enterprise tooling.