The Thin Orchestrator: How We Made Our AI Agents More Reliable by Giving the Router Less to Do
Source
engineering.fb.com
Date
Why it matters
A router that only sequences work and enforces reliability checks, leaving domain reasoning to specialist agents, fixed hallucinationWhen a model states something false with full confidence — inventing facts, citations, or APIs that don't exist.Full definition → problems that bigger context and better prompts did not.
Key takeaways · AI-distilled
Meta's first single AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → for data column onboarding began cross-contaminating subsystems once its context windowThe maximum amount of text a model can consider at once — its working memory for the current conversation or task.Full definition → passed a threshold, applying one subsystem's naming conventions or code patterns to another. Meta says better prompts or a bigger context did not fix it.
Meta's 1,300-line orchestrator routes 20+ workflows to 14 isolated specialist subagents through a fixed 7-step protocol. Only the delegation step uses LLMA large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.Full definition → reasoning; duplicate checks, field gathering and reliability gates are deterministic.
New use cases are added as workflow templates that resequence the same 14 subagents. Meta supported pipeline migration this way with no new agent code.
Subagents load 25+ domain knowledge files at runtime rather than having them embedded in the orchestrator, so a knowledge update takes effect in the next session without orchestrator changes.
Per Meta, configuration prep for a new column took three to four days by hand and about a day with its earlier config-driven system, and now takes under an hour. About two-thirds of the system's changes land without significant human edits.
Terms in this piece · Glossary
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
hallucination — When a model states something false with full confidence — inventing facts, citations, or APIs that don't exist.
context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
