Production AI Agent Orchestration
Built a distributed agent orchestration system for a Series A AI startup, enabling 10k+ daily agent runs with <100ms latency.
Context & Problem
A fast-growing AI agent company was hitting scaling challenges: agents would fail silently, latency was unpredictable, and debugging multi-step workflows was nearly impossible.
Role & Responsibilities
Technical co-founder and lead architect. Designed system architecture, led core engineering team, made hiring decisions.
Architecture & Key Decisions
Implemented a message-driven orchestration layer with persistent state, built on Postgres + Redis + async workers. Added structured logging and tracing across all agent transitions.
Impact & Outcomes
Reduced agent failures by 95%, improved p99 latency from 5s to 200ms, cut operational overhead by 60%. Enabled the company to serve enterprise customers with SLA requirements.
What I'd Do Differently
Invest heavily in observability early. Structured logging and distributed tracing were more valuable than the orchestration logic itself. State persistence patterns matter more than clever scheduling algorithms.