Monitor AI Agents: Catch Unsafe Actions Early
This guide shows you how to monitor AI agents in production so you can detect unsafe or unintended actions before they cause real damage. You’ll learn a practical, layered approach combining logging, real-time rule checks, human review, and automated rollback. By the end, you’ll have a concrete monitoring pipeline you can implement today.
What You’ll Need
- Access to the AI agent’s execution logs (at minimum: input, output, tool calls, timestamps).
- A way to intercept or hook into the agent’s actions (API middleware, proxy, or a wrapper around tool calls).
- A basic rules engine or even a simple script that can evaluate conditions on each action.
- An alerting channel (e.g., Slack, email, PagerDuty) for real-time notifications.
- A human review queue—either a simple spreadsheet or a lightweight ticketing system.
- Write access to a database or log store for storing audit trails.
Step-by-Step
- Define your safety boundary. Write down what actions are absolutely forbidden (e.g., deleting files, sending emails to external addresses, spending money above a threshold, accessing private keys). Also list actions that are risky but sometimes needed (e.g., modifying a database record) so you can require extra approval for those.
- Instrument every action. Wrap every tool call, API request, and file operation the agent makes with a logging function. Capture: the agent’s ID, the exact action, the full input parameters, the timestamp, and the result. Store this in a structured format (JSON lines work well).
- Add a pre-action check hook. Before the agent’s action is actually executed, run it through your rule engine. Start with simple deny rules: if action matches a forbidden pattern (e.g., DELETE on a production table), block it immediately and log the block. If action matches a risky pattern, divert it to a human approval queue instead of executing.
- Implement a post-action anomaly detector. After the action runs, compare the result against expected baselines. For example, if the agent writes a file, check the file size is within normal range; if it calls an API, check the response time and status code. Flag anything that deviates significantly.
- Set up a human review queue for risky actions. For actions that pass the pre-check but are still risky (e.g., sending a message to a customer), place them in a queue. A human must approve before the action is finalized. If you can’t pause the action, at least require a human to acknowledge it within a short time window.
- Create an alerting system. For each blocked or flagged action, send an alert with the full context: what the agent tried to do, why it was flagged, and the relevant log entry. Use severity levels: critical for blocked actions, warning for anomalies.
- Enable automatic rollback where possible. For actions that have a reversible effect (e.g., a database update), store the previous state so you can restore it if a post-action review finds a problem. For irreversible actions (e.g., sending an email), you can’t roll back, so the pre-action check is your only defense—make sure it’s thorough.
- Build a dashboard for live monitoring. Show real-time counts of actions by type, blocked actions, pending approvals, and recent anomalies. This helps you spot trends (e.g., a sudden spike in risky actions) that might indicate the agent is drifting.
- Review logs regularly. Set a daily or weekly review of all flagged actions and a sample of normal actions. Look for patterns that your rules missed. Update your rules and baselines accordingly.
- Test your monitoring pipeline. Run a simulated ‘bad agent’ in a staging environment that tries to do forbidden things. Verify that each action is caught, logged, and alerted correctly. Do this after any major change to the agent or the monitoring code.
Common Mistakes
- Only logging, not acting. Logging is not monitoring. You must have automated checks and alerts, not just a log file that nobody reads.
- Blocking too much. If your pre-action rules are too strict, the agent becomes useless. Balance safety with functionality by using a tiered approach: block the truly dangerous, flag the risky, allow the rest.
- Ignoring context. An action like ‘delete file’ might be fine for a temp file but catastrophic for a database. Your rules must consider the target, not just the verb.
- No post-action verification. Pre-action checks can’t catch everything. A file write that corrupts data is only detectable after the fact. Always compare results to expected outcomes.
- Not testing the monitoring itself. Your monitoring code can have bugs too. If you never test it, you might have a false sense of security.
Tips
- Use a centralized logging service (like ELK or a cloud log aggregator) so you can search and correlate logs easily.
- Make your rules data-driven—store them in a config file or database so you can update them without redeploying code.
- Include a ‘dry-run’ mode in your agent that logs what it *would* do without executing. Use this for testing and for high-risk scenarios.
- For human approval, set a timeout: if no one approves within X minutes, the action is automatically cancelled. This prevents the agent from hanging.
- Monitor the monitor: track how many alerts are false positives. If you’re getting too many, tune your rules. If too few, you might be missing something.
Troubleshooting
No alerts are firing, but you suspect issues. Check your rule engine is actually receiving the action data. Add a debug log to the pre-action hook to confirm it’s being called. Verify your alerting webhook/email is configured correctly.
Too many false positives. Your rules are likely too broad. Add more specific conditions (e.g., only block delete on paths containing ‘production’). Consider using a machine learning anomaly detector that learns normal behavior, but start with simple heuristics.
The agent bypasses the monitoring. This usually happens if the agent calls a tool directly without going through your wrapper. Make sure all tool calls are routed through the instrumented interface. If you can’t wrap everything, at least log at the network level.
Human approval queue is overwhelming. If you’re getting too many approval requests, your risk threshold is too low. Raise the bar for what requires approval, or implement a ‘trusted actions’ list that skips approval for known-safe operations.
Rollback fails. Test your rollback mechanism regularly. Ensure you store enough information to undo an action (e.g., the original value before an update). If rollback is not possible, make sure the pre-action check is extremely strict for that action type.
Frequently Asked Questions
What is the most important thing to monitor?
The action itself—what the agent is about to do. Pre-action checks catch the majority of unsafe actions. Without that, you’re always reacting after the fact.
Can I rely on the agent’s own logging?
No. The agent might not log everything, or it might log incorrectly. You need your own independent logging at the tool-call level.
How do I decide which actions are ‘risky’?
Start with anything that has an external effect (sending messages, making purchases, deleting data) or that touches sensitive systems (production databases, user credentials). Add to the list as you learn from incidents.
What if my agent uses a third-party API that doesn’t allow hooks?
You can still monitor at the network layer (e.g., using a proxy) or by wrapping the API call in your own function. If you can’t intercept, at least log the request/response after the fact and run post-action checks.
Is it enough to review logs after an incident?
No. Post-incident review is useful, but it doesn’t prevent damage. You need real-time pre-action checks and alerts to actually catch unsafe actions before they affect production.
How often should I update my monitoring rules?
At least weekly, or whenever you see a new type of unsafe action. Also after any major change to the agent’s behavior or toolset.
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