AI Agent Reliability Board

Open-source lightweight monitoring for agent failures, hallucinations, and regressions

Problem

More people run AI agents in production, but agent-specific failure modes (bad tool calls, hallucinations, infinite loops) are hard to catch early. Existing monitoring targets traditional services and ignores agent reliability. A Show HN project hints at the space, but the gap remains wide.

Solution

An open-source SDK plus dashboard that instruments each agent step (LLM call, tool use, final output) and automatically computes success rate, hallucination rate, latency, and cost. Alerts when a run fails or deviates, with full trace retention for replay.

Users

Small teams using AI agents for automation, AI engineers, indie developers.

MVP

Python/TypeScript SDK to intercept agent events; local or self-hosted dashboard for run list and detail; basic metrics: success rate, latency, token cost; auto-flagging of failed/anomalous runs; simple webhook alerts.

Revenue

Open-source core plus hosted subscription (by event volume or seat), or enterprise tier with SSO, retention policies, and alert integrations.

Why now

AI agents are moving from demos to production, and reliability is the biggest blocker. Open source builds trust fast; an indie dev can monetize via hosting.