three.dev — AI for Software Development

Debugging AI agents in production is difficult because errors are buried inside conversational traffic rather than showing up as traditional stack traces. three.dev solves this by routing your agent's requests through a proxy to score live interactions without adding response latency. As users interact with your assistant, the platform automatically detects failures, attaches concrete evidence, and groups related errors into root-cause failure modes. When you need to resolve an issue, your coding agent can pull this data over the Model Context Protocol (MCP) to locate the offending code and write a patch. For instance, if your support agent promises a 14-day refund window while your policy documents state 30 days, three.dev surfaces the discrepancy alongside the exact lines of code responsible. After applying a patch, you can compare slices of traffic side-by-side to verify whether the failure rate dropped. While this workflow provides a clear path from production error to code fix, you must route your application's LLM calls through their proxy, which introduces data handling considerations regarding your production traffic. Teams building customer-facing support bots, conversational assistants, or complex multi-turn LLM agents gain a practical way to replace guesswork with concrete evidence when maintaining production prompt reliability.