# Mutagent — Full Context

> Mutagent is an auto-optimization platform for AI agents. It targets the configuration layer that controls how agents behave: prompts, tool descriptions, output schemas, few-shot examples, and evaluation criteria.

## What Mutagent Does

Mutagent automatically diagnoses why AI agents fail by analyzing execution traces, then generates targeted fixes and validates their impact before applying them. All changes require human approval.

It works in three modes:

1. Setup: Install the CLI, connect your observability stack (Langfuse, OpenTelemetry, Opik, Braintrust, Langsmith), map your agents, and define evaluation criteria
2. Optimize: Mutagent diagnoses traces, identifies root causes of failures, generates targeted prompt mutations, validates them against your eval criteria, and presents changes for your approval
3. Watch: Continuous monitoring of production traces with automatic optimization triggers when performance drifts

Key facts:
- First validated fix in under 30 minutes
- Human approval required for all changes (no autonomous modifications)
- Works with LangGraph, LangChain, Mastra, ADK, Claude Agent SDK, and any coding agent
- Integrates with Cursor, Claude Code, VS Code, and Codex
- CLI: npm install -g @mutagent/cli

## What Mutagent Optimizes

- Prompts: where most agent failures originate. A single word change can shift routing accuracy by 25 percentage points.
- Output descriptions: how agents structure their responses
- Tool descriptions: how agents understand and select tools
- Few-shot examples: the examples that guide agent behavior
- Evaluation criteria: the benchmarks that define "good"

## Pricing

- Free: 1,000 insights/mo, unlimited setups and optimizations, full API access, community support
- Scale ($199 to $13,999/mo): 2,000 to 2M insights/mo (tiered), continuous monitoring, advanced analytics, email support
- Enterprise (custom pricing): SLA, dedicated support, on-premise deployment option, custom integrations

One insight equals one analysis of an agent execution. Insights are consumed during setup (developing eval criteria and datasets), optimization (benchmarking, root cause analysis, and validation), and watch (every analyzed production trace).

## Compatibility

Supported agent frameworks: LangGraph, LangChain, Mastra, Google ADK, Claude Agent SDK, custom frameworks

Supported observability: Langfuse, OpenTelemetry, Opik, Braintrust, Langsmith

Supported development environments: Cursor, Claude Code, VS Code, Codex, any coding agent

## Company

Mutagent is a seed-stage company building the AI Agent Engineer. The product is currently in beta.

- App: https://app.mutagent.io
- Docs: https://docs.mutagent.io
- GitHub: https://github.com/mutagent
- Discord: https://discord.gg/mutagent
- LinkedIn: https://linkedin.com/company/mutagent

## Learn — Guides

- Agent API Design: Designing Tool APIs That Survive Production (https://www.mutagent.io/learn/build/agent-api-design/): How to design tool and function-calling APIs for AI agents that survive production: cross-provider schema drift, error payloads, streaming, and context cost.
- Agentic Workflows: The Patterns, and Why They Break in Production (https://www.mutagent.io/learn/build/agentic-workflows/): Agentic workflows chain LLM steps to finish a task. Learn the core patterns, the compounding-error math, and when a deterministic workflow beats an agent.
- AI Agent Orchestration: The Job Is Mostly Knowing When to Stop (https://www.mutagent.io/learn/build/ai-agent-orchestration/): AI agent orchestration coordinates agents through one control loop. Learn the patterns, the termination problem, and what to instrument before you scale.
- AI Agent Design Patterns (and the Way Each One Breaks in Production) (https://www.mutagent.io/learn/build/ai-agent-patterns/): The core AI agent patterns, what each one does, and the specific way each fails in production, with the reliability control that keeps it working.
- Claude Agent Framework: Which Layer of the Stack Do You Need? (https://www.mutagent.io/learn/build/claude-agent-framework/): The Claude agent framework spans the API, Agent SDK, Claude Code, and Managed Agents. See what each layer gives you and where the framework stops.
- Agentic Architecture: Choose Patterns by How They Fail (https://www.mutagent.io/learn/build/multi-agent-architecture/): Agentic architecture explained: the four multi agent architecture patterns, when a single agent wins, and how to contain coordination failures.
- AI Governance Tools for Autonomous Agents (https://www.mutagent.io/learn/observability/agent-governance/): AI governance tools monitor, audit, and enforce policy on AI. Most govern static models. Learn what changes when you must govern agents that take actions.
- LLM as a Judge: The Eval Method Everyone Uses and Nobody Calibrates (https://www.mutagent.io/learn/observability/llm-evals/): LLM as a judge lets one model score another. Learn how to build LLM evals, the documented judge biases, and the judge noise most teams forget to measure.
- LLM Monitoring in Production (the Layer Tracing and Evals Don't Cover) (https://www.mutagent.io/learn/observability/llm-monitoring/): LLM monitoring watches aggregated signals over time to catch drift, cost creep, and regressions before users do. Set thresholds that fire and close the loop.
- What Is LLM Tracing (and Why Your Tracing Tool Might Be Lying to You) (https://www.mutagent.io/learn/observability/llm-tracing/): LLM tracing records every step an AI agent takes so you can debug failures. Learn what to trace, the silent-drop problem, and how to keep signal reliable.
- Prompt Optimization: Why Tweaking Prompts Doesn't Scale (https://www.mutagent.io/learn/observability/prompt-optimization/): Prompt optimization is improving a prompt against a measure, not by feel. Learn the manual techniques, why they stall, and how eval-driven optimization works.
- Prompt Enhancer vs Prompt Optimizer: Only One Can Prove It Helped (https://www.mutagent.io/learn/observability/prompt-optimizer-comparison/): Prompt enhancers rewrite your prompt. Optimizers measure whether the rewrite works. Compare free enhancers, OpenAI and Anthropic tools, DSPy, and Mutagent.
- Prompt Versioning for AI Agents (a Regression Problem, Not a Storage One) (https://www.mutagent.io/learn/observability/prompt-versioning/): Prompt versioning is how you attribute a production regression to a version and roll back safely. Link every version to its eval score and the traces it ran.
- RAG Evaluation: Score the Pipeline, Not Just the Answer (https://www.mutagent.io/learn/observability/rag-evaluation/): RAG evaluation splits into retriever and generator metrics. See which metrics matter, how to build eval sets from traces, and why RAG breaks quietly.
- AI Agent vs Chatbot: Answering Is Not the Same as Acting (https://www.mutagent.io/learn/use-cases/ai-agent-vs-chatbot/): An AI agent acts; a chatbot answers. Learn the real difference, what each is for, and the new failure modes you take on when an agent starts acting.
- AI Agents for Business: The Use Cases That Work (and What It Takes to Run Them) (https://www.mutagent.io/learn/use-cases/ai-agents-for-business/): AI agents for business: the use cases delivering value, why most pilots stall before production, and the reliability work that decides whether you see ROI.
- Customer Service Chatbot: What It Takes to Trust One in Production (https://www.mutagent.io/learn/use-cases/ai-customer-service-agents/): A customer service chatbot is easy to demo and hard to trust at scale. One wrong policy answer is a liability. How to evaluate one for production reliability.
- AI Interviewer Tools: What It Takes to Run One in Production (https://www.mutagent.io/learn/use-cases/ai-recruiting-agents/): AI interviewer tools screen candidates in minutes, but a wrong score is a legal liability. What it takes to run one reliably and pass a bias audit.
- Coding Agents: What They're Good At, and Where They Break (https://www.mutagent.io/learn/use-cases/coding-agents/): Coding agents plan, write, run, and fix code on their own. Learn what they are good at, why they fail, and how to make a coding agent reliable in production.
- What Is an AI Agent? How They Work (and Why They Fail in Production) (https://www.mutagent.io/learn/use-cases/what-is-an-ai-agent/): An AI agent perceives, plans, uses tools, and acts toward a goal across many steps. How agents work, the 5 components, and why production is the hard part.

## Optional

- [Privacy Policy](https://mutagent.ai/privacy): Data handling and privacy practices
- [Terms of Service](https://mutagent.ai/terms): Legal terms of use
- [Blog](https://mutagent.ai/blog): Engineering articles and product updates
- [Partnership Program](https://mutagent.ai/agents-partnership): 1:1 optimization partnership for seed-stage teams