Reading
Agent Eval Harness is a practical benchmarking framework for systematically evaluating the performance of AI agents and RAG workflows in terms of task success rate, latency, cost, evidence quality, and governance compliance.
Section 01
Agent Eval Harness is a practical benchmarking framework for systematically evaluating the performance of AI agents and RAG workflows in terms of task success rate, latency, cost, evidence quality, and governance compliance.
Section 02
Section 03
The AI agent ecosystem is evolving rapidly, but a key question emerges: how to objectively and reproducibly compare the effectiveness of different agents, prompts, tools, and retrieval strategies? The current market is flooded with various agent solutions claiming to be powerful, yet there is a lack of unified evaluation standards.
Teams need a simple way to:
Agent Eval Harness was developed precisely to address these pain points.
Section 04
The framework designs evaluation metrics around six key dimensions:
Section 05
Measures the agent's ability to complete assigned tasks. This is the most core metric, directly reflecting the agent's practicality.
Section 06
For RAG workflows, evaluates the completeness and accuracy of cited sources. Ensures the agent's answers are well-documented and not fabricated out of thin air.
Section 07
Measures whether the agent's response time is within an acceptable range. For real-time interaction scenarios, latency is a key factor in user experience.
Section 08
Tracks the actual cost of agent operation, helping teams make informed trade-offs between performance and cost.
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