AI Agent Error Handling Strategies 2026

Errors are the norm when an Agent executes complex tasks—API timeouts, tool call failures, and incorrect LLM output formats. A well-designed Agent system should have automatic recovery capabilities.

💡 What You Will Learn

Errors are the norm when an Agent executes complex tasks—API timeouts, tool call failures, and incorrect LLM output formats. A well-designed Agent system should have automatic recovery capabilities.

import time

def call_with_retry(fn, max_retries=3):
    for i in range(max_retries):
        try:
            return fn()
        except Exception as e:
            if i == max_retries - 1:
                raise
            time.sleep(2 ** i)  # 1, 2, 4

# AgentSteps
agent = Agent(max_iterations=10)  # 10

# 
def validate_output(output):
    if not output.get("result"):
        return False, "result"
    return True, None

L1: 
L2: Fallback
L3: 
L4: 

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Summary