Building production AI agents often means debugging silent failures—they don't crash, they just produce incorrect outputs. Kelet addresses this by automating the tedious process of sifting through traces for patterns across hundreds of sessions. The core idea shifts from inspecting random individual errors to clustering failure hypotheses to reveal systemic root causes.

Kelet ingests traces alongside user signals, such as sentiment or explicit edits, treating these clues like a detective. It then generates hypotheses about what went wrong and surfaces these clustered issues, often proposing a suggested fix, sometimes even a prompt patch. Integration is streamlined via a CLI skill or standard SDKs for Python and TypeScript.

Unlike standard observability tools that merely report symptoms, Kelet claims to diagnose and prescribe solutions for agentic loop issues. The service is currently free during its beta period, requiring no credit card to connect initial agents. This offers a direct alternative to engineers spending a reported 30% of their week manually analyzing broken traces.

Engineers controlling their own agent stacks, including those using frameworks like LangChain or CrewAI, can leverage Kelet’s continuous analysis engine. The service runs on Kelet’s SOC 2 certified infrastructure, analyzing data privately to ensure isolation. Kelet covers the token costs for its internal analysis, billing users based on usage volume instead.