Agentic AI · Platform
An Autonomous Documentation & Metadata Agent
How an AI agent turned perpetually-stale platform documentation into living, trustworthy metadata that regenerates itself — and why that changed how the whole team worked.
Problem
Documentation for the data platform was permanently out of date. Every new engineer lost days piecing together what tables meant and where data came from, and every stale doc eroded a little more trust in the platform as a whole.
Business Context
The platform was growing faster than any human could document it. Leadership wanted to scale the team, but each new hire was expensive to onboard precisely because the institutional knowledge lived in people's heads, not in anything you could read.
Architecture
I built an agent that reads the sources of truth the platform already had — schemas, dbt models, and column-level lineage — and uses an LLM grounded in that context to generate human-quality documentation. A scheduled run keeps it continuously in sync, and a retrieval layer lets engineers ask questions of the documentation in natural language.
Technical Decisions
The agent generates from ground truth rather than from scratch, so it can't drift into fiction. Output is committed as reviewable pull requests, keeping a human in the loop. Everything the agent asserts is traceable back to the schema or lineage it read.
Trade-offs
Generating from structured metadata means the docs are only as rich as the metadata beneath them — the agent won't invent business context that doesn't exist anywhere. We accepted that limit in exchange for documentation you can actually trust, and it nudged teams to enrich their metadata at source.
Implementation
We proved the pattern on one high-traffic domain, measured whether engineers actually trusted the output, then generalised the readers and prompts into a reusable component that ran across every domain on the platform.
Challenges
The core risk was confident-but-wrong descriptions. Tight grounding in schema and lineage, plus strict prompting and human review on the generated pull requests, kept hallucinations out of anything that shipped.
Performance Improvements
Documentation went from a snapshot that decayed the moment it was written to something that regenerated with the platform. Engineers stopped asking 'is this doc still true?' because the answer was reliably yes.
Business Value
New engineers onboarded dramatically faster, trust in the platform recovered, and the team captured institutional knowledge in a durable form instead of losing it every time someone left.
Future Enhancements
Extending the agent from describing the platform to proactively flagging gaps — undocumented critical tables, orphaned models, and lineage dead-ends — so quality improves continuously rather than on demand.
Let's build something intelligent together.
Whether you're modernising a data platform or bringing agentic AI into production, I'd love to hear what you're working on.