AI Agent Monitoring: How to Catch Loops, Tool Errors and Runaway Costs
By Muhammad Usman · · 7 min read
Why agents need different monitoring
A normal program either works or throws an error. An AI agent can do something much stranger: call the same tool twenty times, give up silently, spend ten dollars on one request, or report "done" when the task wasn't done. Traditional uptime monitoring sees none of this.
What to record for each run
- Goal: what the agent was asked to do
- Steps: each model call and tool call, with inputs, outputs and errors
- Duration and cost per step and in total
- Status: success, error, timeout, cancelled
- Final output: what the agent returned
Most agent frameworks (LangChain, the OpenAI Agents SDK, the Claude Agent SDK, CrewAI) make these easy to collect at the end of a run.
The checks that matter
- Loops: the same tool called repeatedly with the same input. This is the most common costly failure.
- Tool errors: failed API calls, timeouts and permission errors, especially when the agent ignores them.
- Ended on an error but reported success: the last step failed but the run says "success".
- Empty output: a successful run with nothing in the final answer (a silent failure).
- Limits: too many steps, too long, or more expensive than your budget per run.
- Goal not met: the final output doesn't do what the goal asked.
- Ungrounded output: the agent claims results the tools never returned, for example "refund issued" when the refund call failed.
The first five can be checked with simple rules. The last two need an AI review that reads the goal, the steps and the output.
Set limits per agent
Decide per agent what "normal" means: maximum steps, maximum seconds and maximum cost per run. A support agent might need 10 steps and 30 seconds; a research agent might need 60 steps and 5 minutes. Alerts based on your own limits are far more useful than generic ones.
Alerting without noise
- Alert immediately on high-severity issues: loops, runs over budget, goal not met, ungrounded claims.
- Group repeated issues into one incident per agent and problem, and close it automatically when runs are healthy again.
- Send a weekly summary with the success rate, average cost and the most common issues.
How ProofMyAI monitors agents
ProofMyAI takes one HTTP call at the end of each run (see the API docs), checks it for loops, tool errors, limits, empty output, goals not met and ungrounded claims, gives each run a score, and alerts you when something needs attention.
✅ Key takeaways
- Record the goal, every step, errors, duration, cost and final output of each run.
- Rules catch loops, tool errors, empty output and limit breaches.
- An AI review catches goals not met and claims the tools never supported.
- Set limits per agent and alert only on what matters.
❓ Frequently asked questions
What is the most common AI agent failure?
Loops: the agent calls the same tool with the same input again and again, wasting time and money without making progress.
How do I stop an AI agent from overspending?
Set a maximum number of steps, maximum run time and maximum cost per run in your agent code, and alert when a run exceeds them.
Which frameworks can be monitored?
Any framework. Send the run's goal, steps, final output, status, duration and cost with one HTTP call at the end of the run.