Mutagent provides an AI engineering platform that automatically analyzes production traces from AI agents to diagnose failures, derive evaluation suites, and propose verified fixes as pull requests. The platform integrates with major observability tools and agent frameworks, running locally through a user's existing coding agent to keep data on-premise. It includes AgentSpec, a YAML-based specification standard that enables generating fully tested agents from context.
Funding
Funding not disclosed
Founders
Product
Problem
AI agents in production fail in ways that are difficult to diagnose, with failures buried across thousands of traces. Generic LLM-as-a-judge evaluation tools produce inconsistent scores, and coding agents that review traces one at a time cannot identify population-level failure patterns. Teams lack a reliable way to distinguish real quality improvements from random noise, and struggle to build trustworthy evaluation suites that match their domain experts' judgment.
Solution
Mutagent provides an agentic AI engineer that works on a continuous loop to build, evaluate, diagnose, and improve AI agents automatically. The platform analyzes production traces to identify root causes of failures, derives evaluation criteria and custom LLM-as-a-judge graders calibrated to domain experts, and implements guardrails and fixes as pull requests for user approval. It includes the AgentSpec standard, which captures agent intent, context, actions, and evaluation criteria in readable YAML, enabling a generator-verifier loop that builds fully tested agents on any framework. The system runs locally through a user's existing coding agent, integrating with every major observability platform while ensuring data never leaves the user's machine. Each failure found is turned into a deterministic check, growing test coverage while reducing the cost of evaluation over time.
Target Audience
Primary users are mid-to-late-stage engineering teams running AI agents in production with existing observability infrastructure, including AI engineers, platform teams, and ML practitioners who need to verify agent reliability, reduce costs, and maintain trustworthy evaluation systems.
Features
- Automatic trace analysis that clusters entire production histories to identify population-level failure patterns and name root causes on three axes: wrong output, why, and where
- AgentSpec YAML standard that captures intent, context, actions, capabilities, targets, and evaluation contracts for complete agent specification before building
- Generator-verifier loop with separate AI Engineer and AI Architect agents that iterate until every capability is proven present with trace evidence
- Custom LLM-as-a-judge calibration that aligns evaluator scoring with domain expert labels and pins model/temperature for reproducible verdicts
- Failure-derived eval suite that mines MUST and NEVER rules from production traces, converting every confirmed failure into a free deterministic code check
- Runs locally with no data egress, supporting sources including Langfuse, OpenTelemetry, Datadog, raw JSONL, and Claude Code logs
- Integration with agent frameworks including Vercel AI SDK, OpenAI, LangChain, LangGraph, Mastra, Cursor, Codex, and OpenCode
- Fix proposals opened as pull requests on isolated git worktrees, requiring explicit approval before any change is applied