Mirrors
Mirrors is a context-routing platform that helps engineering teams fix recurring AI agent mistakes by injecting targeted corrections directly into live agent conversations. It surfaces mistakes from production traces, lets teams author and preview corrections, and measures the impact of each change on real outcomes. The platform works with major agent frameworks including LangChain, CrewAI, and OpenAI.
- Developer Tools
- Artificial Intelligence
- AI Agents
- Software Only
- Data & Analytics
Funding
Raised to date
$500KRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Founder details are not available yet.
Product
Problem
AI agent teams often see failures and poor outcomes in their trace logs but lack a fast way to act on those insights. Fixing agent behavior typically requires code changes, redeploys, or prompt rewrites, leaving lessons buried in traces and causing repeated mistakes across sessions.
Solution
Mirrors is a context-routing platform that lets teams turn trace insights into actionable guidance for their AI agents. Users analyze sessions alongside outcome labels, author contextual instructions, and preview which past sessions those instructions would match before going live. When a matching situation recurs, Mirrors delivers the context mid-conversation with no code change or redeploy. The platform supports editing guidance, tool descriptions, schemas, and model-facing results, and includes outcome tracking to compare sessions with and without each change so teams can keep what works.
Target Audience
Primary users are engineering teams building and operating AI agents, particularly those working with popular agent frameworks and tracking agent outcomes, who need a faster loop between trace insights and behavior changes.
Features
- Real-time context routing that injects guidance into an agent's conversation when predefined conditions match
- Trace analysis workflow with session review, outcome labels, and feedback integration
- Preview tool showing which historical sessions a new context rule would match before deployment
- No-code deployment: context changes go live without redeploying or editing source code
- Outcome comparison to measure the impact of context changes against tracked metrics
- Broad framework support including LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, Pydantic AI, smolagents, Google ADK, Hermes, AI SDK, Mastra, OpenAI, Anthropic, MCP, Claude Code, Codex, Cursor, and Windsurf
- Capability to edit guidance, tool descriptions, schemas, and model-facing results