Enterprise Agentic AI Platform
Enterprise teams needed AI automation beyond demos — repeatable operational work with governance, evaluation, and observability.
Context
Supply chain and operations teams faced high manual intensity in repeatable workflows across intake, validation, exception handling, and routing.
Architecture
Designed a multi-agent, multi-LLM architecture with retrieval, task routing, evaluation checkpoints, human review paths, and production monitoring.
My ownership
Architecture ownership: system boundaries, orchestration and retrieval separation, governance checkpoints, evaluation strategy, and production-readiness direction.
Evidence boundary
The staffing result is employer-confidential operational context. Public portfolio artifacts demonstrate the architecture pattern, not the employer system or its private 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