Meta builds a swarm of 50+ AI agents to map tribal knowledge across large-scale data pipelines
Meta's AI agents failed to make useful edits on a large proprietary pipeline because they lacked any map of the codebase's tribal knowledge — non-obvious naming conventions, cross-module dependencies, and undocumented rules that existed only in engineers' heads.
Existing AI-powered operational systems could not be extended to development tasks because agents had no understanding of the proprietary config-as-code structure, causing them to produce subtly incorrect code.
The pre-compute engine achieved 100% AI context coverage across 4,100+ files (up from ~5%), documented 50+ non-obvious patterns, and preliminary tests show 40% fewer AI agent tool calls per task; complex workflow guidance time dropped from ~two days to ~30 minutes.
Show all 7 reported metrics
Frequently asked questions
What did this team achieve with this AI workflow?
The pre-compute engine achieved 100% AI context coverage across 4,100+ files (up from ~5%), documented 50+ non-obvious patterns, and preliminary tests show 40% fewer AI agent tool calls per task; complex workflow guid…
What tools did this team use?
large-context-window model.
What results were reported?
AI context coverage: 100%; Codebase files with AI navigation: 4,100+; AI agent tool calls per task: 40% fewer; Complex workflow guidance completion time: ~30 minutes (source-reported, not independently verified).
What failed first in this deployment?
Existing AI-powered operational systems could not be extended to development tasks because agents had no understanding of the proprietary config-as-code structure, causing them to produce subtly incorrect code.
How is this back office ops AI workflow structured?
Explorer agents map codebase → Module analysts analyze files → Writers generate context files → Critic agents review quality → Fixers apply corrections → Orchestration routes engineer queries → Automated self-refresh.