AI Agents · Lesson

Trace Logging for Agent Steps

Logging each reasoning step, tool call, and result for post-mortem analysis.

Why Trace Logging Is Essential for Agents

Standard application logs record errors and events. Agent trace logs record reasoning: what did the agent think at each step, which tool did it choose, what arguments did it use, and what did the tool return?

Without trace logging, debugging an agent failure is like diagnosing a car problem without any dashboard — you can only guess.

Setting Up the Python Logging Module

Python's built-in logging module is the standard tool. Configure it at the start of your agent with a format that includes timestamp, level, and message. Use DEBUG level for trace data — it can be turned off in production.

import logging

logging.basicConfig(
    level=logging.DEBUG,
    format='%(asctime)s [%(levelname)s] %(name)s: %(message)s',
    datefmt='%H:%M:%S'
)

logger = logging.getLogger('myagent')

# Usage:
logger.debug('Step 1: reasoning started')
logger.info('Agent task completed in 5 steps')
logger.warning('Tool returned empty result')
logger.error('Failed to parse tool arguments')

# Output:
# 14:32:01 [DEBUG] myagent: Step 1: reasoning started
# 14:32:03 [INFO] myagent: Agent task completed in 5 steps

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