agent feedback loops

What makes an agent feel alive is that it reacts to what happens. A feedback loop is this basic cycle: the agent takes an action, the world returns a result, and that result shapes the next action. Run a search and the snippets steer the next query; run code and an error message tells you what to fix. Each turn is informed by the last, so the agent steers toward the goal instead of committing to one blind plan.

This is the engine underneath ReAct, reflection, and error recovery alike — all of them are specific ways of feeding an observation back into the next decision. The quality of the loop depends on the quality of the signal: a clear error or a concrete result gives the agent something real to adjust against, while vague or missing feedback leaves it guessing and prone to drift.

Feedback loops cut both ways. A good signal lets an agent self-correct over several turns; a misleading one can lock it into a confident wrong direction, repeating a broken action because nothing told it plainly that it failed. Designing what feedback the agent sees is as important as designing its actions.