Not a pilot and not a prototype. I designed and built an internal AI developer platform wired
into the delivery lifecycle of 300+ microservices in a regulated payments environment.
01 Agent orchestration platform
A Jira story goes in; a reviewed PR comes out.
Four-plane architecture — experience, control, execution, knowledge — on EKS with a LiteLLM gateway handling routing, fallback and per-team cost control. A refinement cockpit turns a Jira story into an execution brief, dispatches it to a sandboxed agent, runs the tests and returns a PR with an architect in the loop.
02 Evaluation & model governance
Agent output that survives a risk committee.
Golden datasets, LLM-as-judge scoring and regression gates on agent output quality, backed by continuous multi-cloud benchmarking across cost, latency and correctness. The layer that makes agent output defensible to a risk committee.
03 AI code review in production
Every diff, every day, reviewed by an agent.
Pull-request review agent running today against every diff as a quality gate inside Azure DevOps pipelines — 20+ PRs a day across 300+ microservices, with cost and model performance tracked per run.
04 Agentic QA
Tests written, run and triaged by agents.
Five agents mapped across a 16-step functional flow and a 9-step performance flow: impact analysis, test-case and automation-code generation, regression triage, and K6 threshold calculation — with the regression gate controlling deployment.
05 Knowledge Center (RAG)
One source of truth every agent reads from.
Postgres-backed retrieval layer with AI-generated per-microservice and product-level documentation, so every agent reasons from one source of truth about how services actually interact.
06 AI governance at bank scale
How a bank says yes to AI.
Founded Deuna's AI Center of Excellence and its intake framework, pioneered Claude on Bedrock in production, and led the joint Deuna–Pichincha evaluation of enterprise AI platforms (Copilot Studio, Gemini Enterprise, Claude).