How to Audit What Your AI Agents Actually Did — Visual Behavioral Proof with PageBolt

How to Audit What Your AI Agents Actually Did — Visual Behavioral Proof with PageBolt An MCP agent chains five tools: browser search, document lookup, Slack notification, API call, and database update. It completes in 8 seconds. Did it do what you asked? Did it touch the right data? Did it expose credentials in a log? You have API response logs. You have database transaction records. You have zero visual proof of what the agent actually saw on screen or did in the interface. That's the governance gap. The LLM Agent Weaponization Risk LLM agents are fast. They're becoming standard infrastructure: CrewAI, LangGraph, Anthropic's Agent SDK, Google Vertex AI agents. Companies are already shipping multi-agent workflows in production — orchestrating 4-6 tools per agent, chaining agents together, running 20+ parallel instances. But fast ≠ auditable. When an agent goes wrong — it deletes the wrong row, leaks PII to a third-party API, takes an action a user didn't authorize — what's your proof?