Ever wonder why AI agents keep missing the big picture in code? Peter Werry shows how a tiny context engine fixes that.
The Simple Version
Think of an AI as a fresh engineer. Without extra context it stops after finding one clue, just like a radiologist who stops at the first sign. Add a context engine and it can scan the whole codebase, pulling the right pieces together.
How It Actually Works
Iceberg Analogy
Demo Walkthrough
Technical Deep Dive
The engine indexes wikis, GitHub history, and Slack threads. It builds a dynamic architecture diagram on the fly and attaches source links for human verification. When paired with Claude Code it reduces cost to under a dollar and cuts task time by half.
Why This Matters
Teams can ship features faster because the agent no longer guesses from incomplete context. Mistakes drop as the system surfaces the right conventions and past decisions. Savings add up across many tasks.
Key Takeaways
- Context engines turn AI from a rookie into a knowledgeable partner.
- They prevent “satisfaction of search” errors that hide critical details.
- Cost drops dramatically when the engine is used.
- Accuracy improves as the agent accesses full tribal knowledge.
- Open‑source tools from Unblocked make the approach reusable.