I built a system that automatically discovers, verifies, and applies relevant requirements from earlier interactions without asking the user where they came from.

The core lesson: just pulling up past history isn't the same as knowing what's actually still accurate. A basic search setup only grabbed 57% of the requirements a coding agent needed. Adding a verification layer pushed that to 100%.

Out of 8 tasks, the baseline got zero right, basic search got 4, and intent-aware search nailed all 8. I did all of this with zero embeddings, zero vector databases, and absolutely no LLM calls in the pipeline.

I set up a coding agent workflow that worked perfectly at first. But once a project got long enough, it started causing problems. When a project passed a few dozen steps, core rules began vanishing. No one deleted them. The context window was not full. Those rules were still technically sitting in the chat logs. They just dropped off the radar because new requests did not trigger the agent to check if an older decision still mattered.

For instance, you might tell the agent on day one to never expose internal database IDs in API responses. Sixty messages later, you ask it to build a new authentication flow. That new request says nothing about IDs. Since the agent lacks a clear reason to look back, it skips that step and ships an endpoint leaking the exact data you tried to protect.

This is not a made up scenario. It is the actual test case I used for this article. Below, I will show you how three different methods handle this exact problem.