Definition: The agent loop is the repeating cycle in which an AI agent reasons about a goal, takes an action such as calling a tool, observes the result and decides what to do next, until the goal is met or a limit is reached.Why It Matters: The loop is what separates agents from single-shot prompts. It lets agents adapt to what they find. Without limits, it can run too long, repeat mistakes or run up cost, so stopping conditions and monitoring are essential.Key Characteristics: Core steps are reasoning or planning, action, observation and evaluation. Loops use memory or state and have limits on iterations, time and cost. They may include checkpoints for human review.
The agent receives a goal and context. It decides the next action, for instance a search. The application executes it and returns the result as an observation. The agent evaluates whether the goal is met. If not, it updates its plan and acts again. The loop stops when the agent produces a final answer, hits a step or cost limit, or needs human input. State is saved each iteration so runs can be resumed, and logs capture every step for review.ProsThe loop enables adaptive, multi-step problem solving.ConsIt can loop indefinitely, repeat errors and accumulate cost.
Research Task: An agent searches, reads, refines queries and writes a summary.Debugging: A coding agent runs tests, reads errors and edits code until tests pass.IT Operations: An agent checks logs, tries an approved fix and verifies the result.
Sense-Plan-Act (1980s): Robotics established the cycle of sensing, planning and acting.Reasoning and Acting Prompts (2022): Research showed language models could interleave reasoning with tool use.Agent Frameworks (2023): Libraries packaged the loop with tools and memory.Current Practice and Trends (2024-Present): Platforms add limits, checkpoints and observability to loops.
When to Use: Use agent loops for open-ended tasks where the steps cannot be known in advance.Designing for Reliability: Set step and cost limits, checkpoint state and detect repeated actions.Operating at Scale: Monitor iterations and cost per run, and review failed runs.Governance and Risk: Log every action, restrict tool permissions and add human approval for high-risk steps.
