AgentReplay — Visual Session Replay & Root Cause Analysis for AI Agents in Production

AgentReplay — Visual Session Replay & Root Cause Analysis for AI Agents in Production

The Problem

When a traditional web app breaks, you open FullStory or LogRocket and watch what the user did. Session replay is a solved problem for UIs. But when an AI agent breaks — when it hallucinates, loops, takes the wrong action, or silently fails — there is no equivalent. Debugging is what one Reddit developer called a "forensic exercise": reading raw logs, tracing API calls, reconstructing the chain of decisions manually.

The pain is acute because AI agent failures are fundamentally different from software bugs. An agent might make a reasonable-looking decision at step 3 that causes a cascade of failures at step 15. By the time you notice, the agent has consumed hundreds of API calls, potentially taken irreversible actions, and the root cause is buried in a haystack of log entries.

Existing observability tools (LangSmith, AgentOps, Galileo) provide traces and metrics. They show you token counts, latency, and tool calls. But they don't answer the question developers actually ask: "What was the agent THINKING at each step, and where did it go wrong?" The gap is between data and understanding — between "here are 47 API calls" and "here's a visual timeline showing the agent decided to retry the same failing approach 12 times because it misinterpreted the error message."