How Meta Used AI to Map Tribal Knowledge in Large-Scale Data Pipelines

Meta built a system using 50+ specialized AI agents to document tribal knowledge in a large-scale data pipeline, enabling AI tools to understand complex codebases without extensive exploration. - A swarm of 50+ specialized AI agents analyzed 4,100+ files across three repositories and languages to create 59 context files encoding previously undocumented tribal knowledge. - The system increased AI context coverage from 5% to 100% of code modules and documented 50+ non-obvious patterns like hidden naming conventions and backward compatibility rules. - Context files follow a "compass, not encyclopedia" principle with 25-35 lines each (~1,000 tokens), containing quick commands, key files, non-obvious patterns, and cross-references. - Preliminary tests showed 40% fewer AI agent tool calls per task and reduced complex tasks from two days to 30 minutes with zero hallucinations in file path references. - The system self-refreshes periodically with automated jobs validating file paths, detecting

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