Monitoring Your AI Agents — How to Know When Something Goes Wrong

What Is Monitoring AI Agents?

When you send an AI agent off to do a task, you can't just walk away and hope for the best. AI agents can stall, repeat themselves, hit wrong answers, or spin off in directions you didn't expect. Monitoring is how you keep tabs on what your agent is actually doing — and catch problems before they waste your time or your money.

Think of it like a security camera for your digital assistant. You set it up, you check it now and then, and if something looks off, you step in. Without monitoring, you're flying blind.

Monitoring an AI agent means watching three main things:

• What it does — What tasks is it trying to complete?
• What it produces — Does the output look right?
• What it costs — Is the agent burning through tokens faster than expected?

Why Monitoring Matters

AI agents are powerful but unpredictable. Unlike a regular program that follows exact instructions, an agent makes choices. Sometimes those choices are smart. Sometimes they loop, fabricate information, or give up halfway through.

Here's what can go wrong if you don't monitor:

• An agent loops in a circle, making the same request over and over — burning tokens the whole time
• An agent hallucinates facts and presents them confidently as true
• An agent runs for 10 minutes when it should have finished in 30 seconds
• An agent completes a task in a way that seems right but actually misses the point

The longer you let an agent run without checks, the more time and money it can waste. For solo founders or small teams, that wasted compute time adds up fast.

How to Monitor an AI Agent

There are a few practical ways to keep an eye on your agents:

1. Build in checkpoints — Break the agent's task into steps, and check the output at each step before it moves on. Don't let it run a 20-step process all at once. If step 3 looks wrong, you can fix it before step 19 wastes time on a broken foundation.
2. Log every action — Give your agent a way to write down what it just did. A simple log file or console output keeps a record you can review. Many frameworks like LangGraph let you inspect the full run history after the fact.
3. Watch token usage — Most AI providers charge by the token. If an agent that should run for $0.05 suddenly hits $3.00, something went wrong. Set spending alerts or review usage after each run.
4. Define success criteria — Before the agent runs, decide what "done" looks like. Is the output a specific format? Does it fit within a certain length? Having clear checks makes it easy to spot when something drifts off course.
5. Use human-in-the-loop pauses — For high-stakes actions — sending an email, approving a purchase, publishing content — build in a pause step where the agent waits for your approval before proceeding.

A Checkpoint Monitor in Python

Here's a simple example of what checkpoint-based monitoring looks like in practice. This uses a basic wrapper function that logs each step, catches errors, and halts the process if something fails.

import traceback

def run_agent_step(step_name, task_fn):
    # Log that the step started
    print(f"[MONITOR] Starting: {step_name}")
    try:
        result = task_fn()
        print(f"[MONITOR] Completed: {step_name}")
        return result
    except Exception as e:
        print(f"[MONITOR] FAILED: {step_name}")
        print(traceback.format_exc())
        return None

# Step 1: Agent does research
output = run_agent_step("research", agent.research)

# Checkpoint: human reviews before proceeding
if output is None:
    print("[MONITOR] Halting — research step failed")
else:
    # Step 2: Agent writes the response
    run_agent_step("write", agent.write)

This pattern — wrap each step in a monitor function — gives you a clear log of what ran, what failed, and where the process stopped. The halting logic means a broken step can't cascade into wasted time on the steps that follow.

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