AI / A CONCEPT NOTE
Execution Loop
the perceive → reason → act cycle that drives autonomous agents
Overview · mechanism
pitfall · examples
01 / THE SHORT VERSION
The idea in a few sentences.
The execution loop (also called the agent loop or cognitive loop) is the core cycle of any AI agent: Perceive (gather observations from tools or the environment), Reason (the LLM decides what to do next based on observations and goals), Act (execute the chosen action — call a tool, return a response, or ask for clarification). This loop repeats until the task is complete or a termination condition is met.
02 / FOLLOW THE MECHANISM
How the perceive → reason → act cycle runs
Perceive
the agent observes its current state — tool outputs, user messages, environment data, and conversation history.
Reason
the LLM processes all observations and decides the next action: 'I need to query the database to answer this question.'
Act
the chosen action is executed — a tool call, API request, or final response generation.
Loop check
was the task completed? If yes, return the result. If no, feed the action's output back into the perceive step and loop.
04 / COMMAND NOTES
Read the command, then the result.
Inspect the flags and arguments before trying an example. Snippets can need local setup, replacement values, or resources in your own environment.
pseudocode for a basic execution loop
python -c "while not done and steps < MAX_STEPS: observation = tool.run(action); action = llm.decide(observation); steps += 1"05 / CHECK YOURSELF
Could you explain Execution Loop to a teammate?
Try it out loud in two sentences: what it is, and the one detail that changes the picture. If you stall, the gap is the part to reread.
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