Handling Agent Failures and Loops
Add timeout limits, maximum iteration caps, and error-recovery prompts to prevent agents from looping indefinitely or calling broken tools repeatedly.
Why Agents Fail and Loop
Agents can get stuck in failure loops for several reasons: a broken tool returns an error the agent doesn't know how to escape, the model generates malformed action syntax repeatedly, a task is impossible given the available tools, or the agent keeps calling the same tool with slight variations hoping for a different result. Without safeguards, this burns API budget and never resolves.
Maximum Iteration Limits
The simplest protection is a hard cap on the number of Thought/Action/Observation cycles. LangChain's AgentExecutor accepts a max_iterations parameter. When the limit is hit, the executor stops the loop and returns a message indicating the agent could not complete the task.
from langchain.agents import AgentExecutor
agent_executor = AgentExecutor(
agent=agent,
tools=tools,
max_iterations=10, # Hard stop after 10 steps
max_execution_time=30.0, # Also stop after 30 wall-clock seconds
early_stopping_method='generate', # Ask the model for a partial answer at the limit
verbose=True
)All lessons in this course
- The ReAct Framework: Think, Act, Observe
- Defining Tools for Your Agent
- Building a ReAct Agent with LangChain
- Handling Agent Failures and Loops