✨ TLDR
Helped an enterprise AI observability platform improve their docs, make them AI-agent-ready, and set up automation to keep everything in sync. Built internal AI agents that monitor code changes and upstream integrations.
The result: docs that stay fresh automatically, and systems that catch breaking changes before they hit production.
🎯 The Problem
This client runs an AI observability and evals platform used by enterprise teams. They were launching a new SDK version and had a few pain points:
- Docs needed a revamp for the new SDK
- Many integrations with external tools that would break when those tools released updates
- No system to catch docs drift or integration issues early
- Wanted their docs to be AI-agent-ready
💡 The Approach
Same philosophy I bring to all my consulting work: deliver value fast, then leave systems behind.
I brought three things:
- User perspective — I use evals tools daily, so I could give real feedback on what works and what doesn’t
- Product taste — spotting DX issues across their SDK and web app
- Systems thinking — automation that compounds, not one-time fixes
🛠️ What I Built
Docs Revamp
Improved the docs structure and made them AI-agent-ready. This means AI coding tools and agents can easily consume and work with their documentation.
Internal AI Agents
This is where it gets interesting:
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Docs auto-update agent — monitors code changes and flags when docs need updating. No more docs drift.
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Integration monitoring agent — the platform has many integrations with external tools. When those tools release new versions, the agent checks if anything needs updating on our side.
Product Feedback
Beyond docs, I gave feedback across their entire surface: SDK, web app. The kind of DX issues that are hard to spot unless you’re actually using the product.
🔗 Related
- AI Consulting — More about my consulting work
- AI-Ready Docs for Ragas — Similar project for an open source evals framework