Agent Loop

What is Agent Loop?

An agent loop is the iterative cycle of observe, reason, act, and evaluate that an AI agent repeats until it completes a task or reaches a termination condition. Each iteration through the loop represents one step of autonomous work: the agent perceives the current state, decides what to do next, executes an action, and assesses whether it is closer to the goal. This loop is the fundamental execution pattern underlying all agentic AI systems.

How does Agent Loop work?

The agent loop follows a consistent four-phase pattern. In the observation phase, the agent reads the current state — tool outputs, environment variables, user messages, or file contents. In the reasoning phase, it analyzes what it has observed, considers available actions, and plans the next step. In the action phase, it executes a tool call, generates text, or modifies state. In the evaluation phase, it checks whether the action succeeded and whether the overall goal is met.

For example, a coding agent's loop might look like: observe (read the error message), reason (identify the likely cause), act (edit the source file), evaluate (run the tests). If tests still fail, the loop continues with a new observation of the updated error output.

Loop termination occurs when the agent determines the goal is achieved, encounters an unrecoverable error, or hits a configured maximum iteration limit. Well-designed loops include backtracking capabilities — if a sequence of actions leads to a dead end, the agent can undo recent steps and try an alternative approach.

Why does Agent Loop matter?

The agent loop is what differentiates agentic AI from single-turn language model interactions. Without the loop, AI systems can only respond to one prompt at a time. With it, they can tackle multi-step problems that require sequential reasoning, trial and error, and adaptive planning — the kinds of tasks that constitute real-world work.

Loop design directly determines agent reliability. Poor loop implementations get stuck in infinite cycles, repeat failed actions, or terminate prematurely. Production agent systems invest heavily in loop engineering, implementing exponential backoff, diverse retry strategies, and progress detection to ensure convergence.

Best practices for Agent Loop

• Set a maximum iteration limit to prevent infinite loops when the agent cannot make progress
• Implement progress detection that identifies when the agent is repeating the same failed action
• Log every loop iteration with observations, reasoning, and actions for debugging and evaluation
• Include a reflection step every N iterations where the agent reassesses its overall approach rather than just the next action