The Thin Orchestrator: How We Made Our AI Agents More Reliable by Giving the Router Less to Do

• Sometimes, giving an AI agent all of the knowledge it needs is the wrong approach.
• At Meta, we’ve seen notable improvements in efficiency and accuracy by keeping the orchestration layer of our data pipeline agents deliberately thin: It routes work and enforces reliability checks, while specialized agents do the domain reasoning.
• We’re sharing our own process and insights, plus how any team can apply this to their production AI agents.

We built a 1,300-line orchestrator that routes 20+ workflows to 14 specialized AI agents. It performs zero domain work itself. That’s the point.

When we first deployed AI agents to automate data column onboarding across Meta’s recommendation systems — a process spanning three code repositories, three programming languages, and up to six pipeline stages that must stay in sync — we did what most teams do: We built one smart agent that knew everything.

It worked in demos. In production, it started hallucinating.