Agentic Loop
The agentic loop is the fundamental cycle through which agents operate: perceive the environment, reason about actions, execute, and evaluate outcomes.
Definition
The agentic loop describes the iterative process through which an agent pursues goals:
Perceive → Reason → Act → Evaluate → (repeat)
This loop continues until the agent achieves its goal, encounters a terminal failure, or requires human intervention. The loop is the atomic unit of agent operation—everything an agent does can be understood as iterations of this cycle.
The Four Phases
Perceive The agent gathers information about its current state and environment. This includes parsing task instructions, reading retrieved documents, observing tool outputs, and noting previous action results.
Reason Using available information, the agent determines what action to take. This involves understanding the goal, assessing progress, identifying gaps, evaluating options, and selecting an approach. Reasoning quality directly impacts agent effectiveness.
Act The agent executes the chosen action. Actions might include generating text, calling tools, making API requests, delegating to sub-agents, or requesting human input.
Evaluate After acting, the agent assesses results. Did the action succeed? Did it advance the goal? Are there errors to handle? Should the strategy change? Evaluation informs the next perception phase.
Why It Matters
Understanding the agentic loop provides a mental model for:
Debugging: When agents fail, you can identify which phase broke down—poor perception (missing information), flawed reasoning (bad decisions), failed action (tool errors), or inadequate evaluation (not catching mistakes).
Design: Agent architectures should support each phase. Perception needs clean data interfaces, reasoning needs appropriate context, action needs reliable tools, evaluation needs clear success criteria.
Optimization: Each phase can be improved independently. Better prompts improve reasoning, better tools improve action, better metrics improve evaluation.
Common Misconceptions
"The loop runs once" Complex tasks require many iterations. A research agent might loop dozens of times: searching, reading, re-searching, synthesizing.
"Each phase is a separate call" In practice, phases often blend. A single language model call might include perception (processing context), reasoning (deciding action), and action specification (generating tool call).
"Evaluation is optional" Without evaluation, agents cannot detect errors or progress. Even implicit evaluation (does output make sense?) is evaluation.