Multi-Agent AI Platform / observability & governance

Seeing what agents actually do

An internal multi-agent platform built on FastAPI, LangGraph, and DeepAgents was scaling fast — and losing visibility just as fast. I owned the observability strategy end-to-end: architecting and hardening a self-hosted MLflow rollout, and establishing distributed-tracing standards across every LangChain/LangGraph agent so each agent’s execution history is isolated and debuggable.

What changed

• Eliminated trace-loss incidents in production, unblocking safe scaling of agent workloads.
• Shipped governance and cost-visibility tooling — an admin dashboard with per-message cost tracking — adopted by engineering leadership to monitor AI spend platform-wide.
• Extended observability beyond the core platform with a federated-tracing proxy, so external agent integrations report telemetry centrally.