Agentic Loop

An agentic loop is the fundamental execution pattern of AI agents. It consists of a repeating cycle: the agent observes its current state (including results from previous actions), reasons about what to do next (using its language model), takes an action (calling a tool or generating output), and then loops back to observation. This continues until the task is complete, a maximum iteration count is reached, or the agent determines it cannot proceed. The loop typically includes several phases in each iteration: perception (processing new information from tool results, user input, or environmental changes), reasoning (analysing the situation and planning the next step), action selection (choosing which tool to call or what response to generate), and execution (performing the selected action and collecting results). Safety mechanisms within the loop include maximum iteration limits (preventing infinite loops), budget controls (limiting token or API call spending), human approval gates (pausing for confirmation before high-impact actions), and error handling (detecting and recovering from failed actions).

The agentic loop is what enables AI to handle open-ended, multi-step tasks. Unlike single-turn interactions where the model generates one response, the loop allows the agent to iteratively work towards a goal, adapting its approach based on intermediate results — much like a human would. Understanding agentic loops helps business leaders assess the capabilities and limitations of AI agent products. The quality of the loop implementation — how well it handles errors, how efficiently it uses tokens, how reliably it converges on good results — directly determines the agent's reliability and cost-effectiveness. For organisations building custom agents, loop design is a critical architecture decision. Choices about maximum iterations, tool selection strategies, memory management, and stopping conditions significantly affect agent behaviour and user experience.