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.…