Don't Panic — Agent Loop Engineering

Agent Loop Engineering is the work of designing the cycle around an AI model: it reads the current situation, decides on a move, takes an action, sees what happened, and either continues or stops. The model is not the whole machine. It is the part with opinions. The loop is the part that makes those opinions arrive somewhere useful.

Before this arrangement, a model could answer a prompt once and then sit there, looking intelligent in the manner of a very expensive decorative lamp. An agent loop lets it use tools, read the result, and change course. That is useful for a task whose next step depends on what the previous step found. It is also where the small administrative detail of reality arrives: tools fail, context grows, and a cheerful model may continue cheerfully past the point of usefulness.

The useful shape has four pieces. Observation is the current context: the request, instructions, history, and tool results. Reasoning chooses the next step. Action calls a tool or changes state. Decision checks whether the task is complete, blocked, or going in circles. Keep those pieces distinct. A system that cannot say what it observed, what it did, and why it stopped is not mysterious. It is merely difficult to debug.

The surprise is that stopping is not the unglamorous bit at the end. A termination condition is a design feature. A final answer can end the run, but so can a maximum iteration count, a wall-clock timeout, an unrecoverable error, or repeated lack of progress. An agent that never stops has not become ambitious. It has become a billing event with a keyboard.

Context needs the same stern attention. Every turn adds action history and tool results, so the prompt can eventually carry more luggage than the task needs. Context evolution means deciding what remains in view, what becomes a summary, and what lives outside the prompt for retrieval. Keep the durable state explicit; give the model the compact observation needed for this turn.

Recovery is another part of the loop, not a heroic afterthought. Error reflection, output validation, retry with backoff, alternative paths, and human escalation all exist because tools and models fail in different ways. Start with one bounded loop, a few tools, clear state, and logs. Add phases, parallel work, or manager-and-worker arrangements only when the task genuinely needs them.

Read the Introduction for the full control path and failure behavior. Use the Slides when you need the relationships at a glance, then keep the Cheatsheet nearby when choosing stop rules, context strategies, and recovery patterns. The Reference tab leads to the ReAct paper and the agent-building guides that explain why this compact-looking loop contains so much engineering.