We've all been there: you ask an AI agent to do something straightforward, and it delivers something bafflingly wrong. Not because the model can't reason — but because it never had the information it needed to reason well.
Context is the single biggest lever in agent quality. A model with perfect recall and zero context is useless. A model with mediocre recall and rich context is surprisingly capable. Yet most agent architectures treat context as an afterthought — a system prompt bolted on at the last minute.
At Loopwork, we've found three patterns that consistently separate agents that people actually use from the ones they abandon after a single session:
First, give agents access to the real artifacts. Don't summarize your codebase into a paragraph. Let the agent read the files, run the tests, see the errors. The closer an agent gets to the source of truth, the better its output.
Second, make context retrieval dynamic. Static system prompts are a starting point, not a strategy. The best agents pull in context as they work — fetching docs, reading logs, checking recent changes — just like a good engineer would.
Third, close the feedback loop. When an agent produces output, it should be able to see what happened next. Did the code compile? Did the test pass? Did the user accept the change? Without this signal, agents can't self-correct.
The models will keep getting better. But the gap between a good agent and a bad one will always come down to context. That's where we're focused.