Most companies get stuck in the same place: the model is fine, the tools are there, but the agent does not know how our company does this particular thing. The fix sounds thoroughly unglamorous — write institutional knowledge into structured documents and connect them to the agent over a standard protocol.
A repeatable approach
The approach Skai Labs published is this: create a GitHub repository, write the process, conventions and common traps in structured Markdown, and connect it to Claude Code over MCP. Once connected, a five-person team produced automation scripts two to ten times faster. No model change, no system rewrite.
Why it works
The agent’s bottleneck was never writing code; it is missing context. Writing down how we do this, when to stop and who to confirm with turns a senior colleague’s judgement into a searchable asset. The more a document reads like an operating manual, the fewer mistakes the agent makes.
The counter-evidence
In LocalStack’s 2026 survey, 65% of teams at least doubled code output, yet 54% reported more defects in AI-generated code and about 40% of AI code never reached production. Much of the gap between output and delivery is exactly this: not enough context was given.
What it means for enterprises
- Pick the most repetitive process first — reconciliation, weekly reports, data sync — and write it as a structured document.
- Write for a machine: what the input is, what the steps are, the decision conditions, where the boundary lies, and what to do on failure.
- Hand over through a protocol, not screenshots or word of mouth; change it once and it takes effect everywhere.
- Write back after every run — the knowledge base is alive, or in three months it is an outdated document again.
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