Agentic AI

How do I evaluate an agentic AI system?

Standard reference-based metrics do not apply. Track task-success rate (did it complete the goal), trajectory quality (were the intermediate steps sensible), cost-to-solution (tokens + tool calls), and adherence to guardrails. Use LLM-as-judge for trajectory quality, human review for high-stakes outputs.

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For developers

Call this framework and its tools from your own agent via the Model Context Protocol (MCP) server. Works with Claude Desktop, Cursor, Zed, Continue, and the OpenAI Agents SDK.

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