Moyai is an AI reliability platform that detects behavioral anomalies in LLM agent traces, distinguishing real failures from harmless deviations. It connects to existing observability stacks like Langfuse, LangSmith, and OpenTelemetry, then uses an LLM-as-a-verifier to validate issues and routes alerts to Slack. The platform requires no new SDKs or rule-writing, delivering reliability insights in minutes.
Funding
Funding not disclosed
Founders
Product
Problem
Observability tools show what an agent did but not what is off, leaving teams to manually scan thousands of traces for hidden problems. Evals only catch anticipated failures, so unanticipated issues like wrong tool calls, context loss, or confident incorrect answers ship to production undetected. This gap means customers or regulators often become the de facto monitoring system, with failures compounding across similar requests for days before discovery.
Solution
Moyai automatically reads existing agent logs from Langfuse, LangSmith, or any OpenTelemetry-compatible source and surfaces behavioral anomalies without requiring new SDKs or rule definitions. The platform uses structural and semantic clustering to group traces by behavior and identify outliers, then applies an LLM-as-a-verifier to determine whether each anomaly is a genuine failure or just a benign behavioral shift. Verified problems are routed to team communication channels like Slack, enabling rapid response. This approach turns raw trace data into actionable reliability insights, helping teams catch rogue agent behavior before it reaches end users.
Target Audience
Primary customers are AI engineering and machine learning operations teams building LLM-powered applications who need to detect and resolve agent behavioral failures beyond what traditional observability and evals can catch.
Features
- Automatic anomaly detection through structural and semantic clustering of agent traces, with no rules or failure definitions required upfront
- LLM-as-a-verifier pipeline that distinguishes real agentic failures from benign behavioral shifts without manual configuration
- Native integrations with Langfuse, LangSmith, and OpenTelemetry-compatible tools, requiring minimal code changes
- Automated alert routing to Slack or other messaging platforms for immediate team notification
- Cloud-based analysis that reads existing logs, eliminating the need for new SDKs or additional instrumentation