Agent Error Handling
Strategies for detecting, recovering from, and learning from errors that occur during agent execution, including tool failures, reasoning errors, timeout exceptions, and unexpected model outputs.
Agent error handling is more complex than traditional software error handling because errors can be non-deterministic, ambiguous, and self-inflicted. A tool API might return an unexpected format. The model might misinterpret a tool response. A reasoning chain might lead to a dead end. Each error type requires a different recovery strategy.
For reliable agent systems, implement error handling at every layer. Tool-level error handling catches API failures, timeouts, and malformed responses with retry logic and fallback options. Reasoning-level error handling detects when the agent is stuck in a loop or pursuing an unproductive path, triggering a strategy reset. System-level error handling manages resource exhaustion, model API outages, and infrastructure failures. The most important pattern is graceful degradation: when a tool fails, the agent should try alternative approaches rather than failing the entire task. Log errors with full context (what the agent was trying to do, what it observed, what went wrong) to build a knowledge base of failure modes that informs future improvements.
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.