Agent Decision Logger

A monitoring tool that logs what AI agents rejected or didn't choose, not just what they executed. Helps teams debug agent behavior and prevent costly mistakes by showing the full decision tree of autonomous systems.

AI agent deployments are accelerating fast in 2025, and the tooling for observability hasn't kept pace — most teams are flying blind on why agents took one path over another, which matters enormously when those agents are touching production systems, customer data, or financial transactions. LangSmith covers execution traces reasonably well, but rejected-path logging is largely absent from the current observability stack, making this a genuine gap rather than a crowded space. The $2k–10k/month band is plausible given it targets engineering teams at companies already spending on agent infrastructure, where a single prevented mistake easily justifies the cost. The biggest risk is that the major platforms — LangChain, LlamaIndex, or the cloud providers — absorb this into their native observability offerings before there's time to build a defensible customer base.