How to Monitor AI Agents After Launch

Passing tests is not enough. Once an AI agent is live, you need to monitor how it behaves in real workflows.

In this video, we break down production monitoring for AI agents: traces, tool-call behavior, human approvals, override rates, drift signals, cost, latency, retries, and boundary events.

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We cover:

- Why passing tests does not mean an agent stays safe
- What production monitoring means for AI agents
- How to trace every decision path
- How to monitor tool-call behavior
- How to use human approvals and overrides as feedback
- How to detect drift in outputs and confidence
- Why cost, latency, and retries are behavior clues
- How to alert on permission and boundary events
- A cybersecurity example using a vulnerability triage agent
- A practical AI agent monitoring dashboard checklist

This is a defensive security automation example using synthetic local data only. No exploit steps, no payloads, and no offensive instructions. The goal is safer workflows, clearer decisions, and human-reviewable automation.

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Chapters:
0:00 Intro
0:02 Passing tests is not enough
0:42 Monitor the path, not just the status
1:26 Trace every decision path
2:11 Tool-call behavior
2:55 Human approvals and overrides
3:35 Detect drift early
4:22 Cost, latency, and retries
5:00 Boundary events and alerts
5:38 Vulnerability triage monitoring
6:29 Monitoring dashboard checklist
7:09 Make failure visible

#AIAgents #AIAutomation #Cybersecurity

Видео How to Monitor AI Agents After Launch канала CyberRiderX