Agentic Loop

The Agentic Loop is the core operational pattern of AI agents: a cyclic sequence of perceive → plan → act → observe that repeats until the task is complete. In each iteration, the agent receives its current state (what it knows, what tools returned last, what the user asked), reasons about what to do next, selects and executes an action (calling a tool, generating text, making a decision), observes the result, and updates its understanding before beginning the next iteration. Unlike a single-pass LLM call that takes one input and produces one output, an agentic loop allows an AI system to handle tasks requiring adaptive multi-step reasoning — where the right next action depends on what was discovered in the previous step.

The loop continues until a terminal condition is reached: the task is complete, the agent determines the goal cannot be achieved, a maximum iteration count is hit (a common safety guard against infinite loops), or the agent explicitly requests human input. The number of iterations needed varies dramatically by task: a simple research task might need 3-5 loops; a complex multi-step workflow might require 50+ iterations across minutes or hours. Long-running agentic loops raise reliability engineering concerns: each step introduces potential for errors that can compound, requiring robust error handling, retry logic, and checkpoint mechanisms so that a failure in iteration 47 doesn't require starting over from iteration 1.

For B2B teams deploying AI agents, understanding the agentic loop clarifies both the power and the risk of autonomous systems. The power is that the loop allows an AI to adaptively pursue a goal, adjusting its approach based on what it discovers — far more capable than fixed multi-step pipelines. The risk is that errors in early steps can propagate through subsequent iterations before being detected, and the agent's decisions in the middle of a loop may be harder to audit than single-turn interactions. Production agentic systems benefit from logging every loop iteration, making tool calls and their results visible for debugging, and designing human checkpoint triggers for high-stakes or irreversible actions within the loop.