Sentrial

Teams deploying AI agents in production lack visibility into how those agents actually behave across sessions, tool calls, and LLM interactions. Without tracing, failures like tool timeouts, hallucinated tool arguments, or user frustration go unnoticed until users report them or quietly drop off. Debugging these issues is hard because teams cannot easily link an agent failure back to the specific code or prompt causing it. The result is silent drift and regressions that surface to users before the team is aware anything is wrong.

Sentrial traces every agent session, capturing user inputs, tool calls, LLM responses, latency, and token costs at each step so teams can see exactly how an agent behaved. It supports automated flags and assertions, such as detecting user frustration, tool schema hallucination, or goal abandonment, to catch regressions before users notice. The product also lets teams A/B test prompts, models, and configurations in production with success rate and latency comparisons across variants. It sends real-time Slack alerts when agents hit error spikes or tool timeouts, and can pinpoint the failing code (e.g. a specific function causing a timeout) with a suggested fix.