Enterprise Agentic AI Platform
Enterprise teams don't need another demo. They need repeatable operational work with governance, evaluation, and observability built in.
Context
Supply chain and ops teams were doing intake, validation, exception handling, and routing by hand — work that repeats, so it should have been automated already.
Architecture
I designed a multi-agent, multi-LLM architecture: retrieval, task routing, evaluation checkpoints, human review paths, production monitoring.
My ownership
I owned the system boundaries, the orchestration/retrieval split, governance checkpoints, evaluation strategy, and the call on when it was production-ready.
Evidence boundary
The staffing number is real; the system behind it is employer-confidential. What's here is the architecture pattern, not private employer records.
Key Tradeoffs
- Separated orchestration from retrieval so model and tool layers evolve independently
- Added human approval gates for high-risk actions instead of fully autonomous execution
- Invested in evaluation harnesses early rather than relying on anecdotal QA