Agent Loop
The agent loop is the cycle an AI agent repeats while pursuing a goal: observe the current state, plan the next action, act through a tool, check the result, then continue, re-plan or stop.
No jargon, no math
In plain words, what is it?
Each turn the agent reads what changed since last time (observe), picks the single next action that gets closer to the goal (plan), executes it through a tool (act), and reads the outcome (check). Then the cycle restarts from the new state. A chatbot runs the loop zero times: one question, one answer, done. An agent runs it as many times as the goal needs, with the stop conditions you set: goal reached, step limit, budget limit, or a boundary that hands off to a human. The loop is where both the power and the failure live: no stop condition and it retries forever, small unchecked deviations compound into drift, and every turn costs tokens and seconds.
- 01
Observe. Read the current state: results so far, errors, new data.
- 02
Plan. Choose the next action that moves the goal forward.
- 03
Act. Execute it through a tool: an API call, a query, a click.
- 04
Check. Did it work, and what changed?
- 05
Loop or stop. Success feeds the next turn; goal, limit or boundary ends the cycle.
Terms that live nearby.
AI Agent
An AI agent is software that pursues a goal on your behalf: it reads the situation, plans the next step, acts through tools (email, calendars, databases, APIs) and checks the result before continuing, instead of answering one question and stopping.
ReadTool Use
Tool use is the capability of an AI model to do more than write text: it can call external tools, such as searching a database, sending an email or running a calculation, by emitting a structured request that software executes for real.
ReadAgent Memory
Agent memory is the store that lets an AI agent remember what it has done and learned across steps and sessions: recent conversation, facts it has saved, and outcomes of past tasks, so it does not start every interaction from a blank page.
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