An agent loop is a fundamental execution pattern in autonomous AI systems that implements a sense-think-act cycle through iterative interaction between a language model and external tools. The agent loop continuously assembles contextual information, invokes the model for decision-making, parses generated tool-use requests, applies permission and safety constraints, executes approved operations, and repeats this sequence until a text-only response is produced suitable for presentation to users ¹⁾.

The agent loop implements a structured iteration pattern that separates reasoning from action execution. At each cycle, the system maintains a state that includes the user's original request, accumulated context from previous interactions, and results from tool executions. The loop queries a language model with this complete context, receives structured responses that may include tool invocations, validates requested operations against predefined constraints and permission gates, and conditionally executes approved tools before incorporating their results back into the context ²⁾.

This architecture demonstrates that production complexity in AI agents emerges not from core reasoning mechanisms but from the supporting infrastructure: permission systems, error handling, state management, tool routing, and graceful degradation patterns. The reasoning component itself—the language model's ability to generate appropriate tool-use requests—represents only a portion of the complete system.

The agent loop follows a predictable sequence: (1) Context Assembly — collecting the user query, relevant retrieved documents, previous turn results, and system constraints into a unified prompt; (2) Model Invocation — calling the language model with assembled context to generate the next action; (3) Response Parsing — extracting structured tool-use requests from model output, typically through standardized formats like XML or JSON; (4) Permission Evaluation — checking requested operations against access control policies, rate limits, and safety constraints; (5) Tool Execution — running approved operations and capturing their results; (6) Loop Control — determining whether to continue iteration (return to step 1 with updated context) or terminate with a final response ³⁾.

The async nature of the loop enables concurrent tool execution and long-running operations without blocking the reasoning process. Termination occurs when the model generates text-only responses without tool-use requests, indicating task completion or impasse.

Effective agent loops require careful attention to error handling and recovery mechanisms. Tools may fail due to transient errors, permission denials, or invalid parameters. The loop must capture these failures and present them back to the model for remediation—either reformulating the request, attempting alternative approaches, or explaining limitations to users ⁴⁾.

Context management becomes critical as loops execute over many iterations. Token budgets limit how much historical information can be retained, requiring techniques such as summarization, selective truncation, or hierarchical context organization. Some implementations maintain separate short-term working context and long-term episodic memory to balance relevance and capacity constraints.

Permission gates implement the security boundary between model reasoning and external system access. These may include: user-specific authorization rules, rate limiting policies, audit logging of all tool invocations, and graduated trust mechanisms that increase permissions as confidence in request validity increases.

The agent loop pattern connects to broader agent architecture frameworks including planning layers that decompose complex goals into intermediate steps, memory systems that persist information across multiple conversations, and reward mechanisms that guide learning from tool execution outcomes. Chain-of-thought prompting enhances the reasoning quality within loops by encouraging explicit intermediate reasoning steps. Retrieval-augmented generation extends loops with external knowledge access, while reinforcement learning from human feedback (RLHF) optimizes the quality of tool-use decisions through iterative improvement ⁵⁾.

Production implementations of agent loops power code synthesis systems, research assistants, and task automation platforms where repeated reasoning-action cycles enable complex problem decomposition.

• Agentic Feedback Loop
• How to Create an Agent
• AI Agents
• Multi-Agent Systems
• Agentic Workflows