Everyone wants you to "add AI." Nobody can tell you where it belongs — or where it will quietly make things worse.

The situation

There’s pressure to “do something with AI,” and a graveyard of proofs-of-concept that never reached production because the data underneath wasn’t ready, the cost was unbounded, or nobody could evaluate whether the output was any good. Most teams also don’t know where to start technically — how an MCP server actually connects an agent to real data, whether Copilot CLI or a VS Code agent fits their workflow better, or why a repo full of undocumented context makes every agent worse, not better.

What Enablement does

I set up the actual tooling — MCP servers, coding agents in VS Code or via GitHub Copilot CLI, and Azure AI Foundry if you’ve already got it — and structure your repository so agents get the context they need without prompt bloat, using a hierarchical skills.md / instructions.md pattern. Then I adapt one proven pattern to your repo as a concrete, working example: an AI-agentic PR-review agent built on GPT-5-mini specifically because it’s cheap enough to run on every pull request without a meaningful per-review cost. You walk away with both the infrastructure and something already running on it.

What you walk away with

• A repo and team actually set up to use AI agents — MCP servers, coding agents, and a knowledge structure that avoids context bloat.
• One production pattern, adapted to your codebase — the same low-cost AI PR-reviewer pattern running on my own engagements right now.
• A clear, honest read on whether your data is AI-ready, and what to fix first if it isn’t.