Agent Loop
The core execution cycle of an AI agent where it repeatedly processes input, reasons about next steps, executes actions, and evaluates results until a goal is achieved or a termination condition is met.
The agent loop is the fundamental runtime pattern for all agentic AI systems. In its simplest form: receive input, think about what to do, take an action (call a tool or generate output), observe the result, and decide whether to continue or stop. This cycle repeats until the agent determines the task is complete, encounters an unrecoverable error, or hits a resource limit.
For engineering teams, the agent loop design has major implications for reliability and cost. Key decisions include maximum iteration limits (to prevent infinite loops), termination criteria (how the agent knows it is done), error handling strategy (retry, skip, or abort on tool failures), and state management between iterations. A well-designed agent loop includes circuit breakers, cost tracking per iteration, and structured logging of each think-act-observe cycle. Most agent failures in production trace back to loop control issues: agents that do not know when to stop, get stuck retrying failed actions, or loop without making progress.
Related Terms
Model Context Protocol (MCP)
An open standard that defines how AI models connect to external tools, data sources, and services through a unified interface. MCP enables agents to dynamically discover and invoke capabilities without hardcoded integrations.
Tool Use
The ability of an AI model to invoke external functions, APIs, or services during a conversation to perform actions beyond text generation. Tool use transforms language models from passive responders into active problem solvers.
Function Calling
A model capability where the AI generates structured JSON arguments for predefined functions rather than free-form text. Function calling provides a reliable bridge between natural language understanding and programmatic execution.
Agentic Workflow
A multi-step process where an AI agent autonomously plans, executes, and iterates on tasks using tools, reasoning, and feedback loops. Agentic workflows go beyond single-turn interactions to accomplish complex goals.
ReAct Pattern
An agent architecture that interleaves Reasoning and Acting steps, where the model thinks about what to do next, takes an action, observes the result, and repeats. ReAct combines chain-of-thought reasoning with tool use in a unified loop.
Chain of Thought
A prompting technique that instructs the model to break down complex problems into sequential reasoning steps before producing a final answer. Chain of thought significantly improves accuracy on math, logic, and multi-step tasks.