Task Success Metrics
Learn to define and measure what success means for your AI agents
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0 / 5 completedWhat Does Success Mean?
"Did it work?" seems like a simple question, but defining "work" for AI agents is surprisingly nuanced. Does success mean the task completed? That the output is accurate? That users are satisfied? That it happened efficiently? All of the above? Task success metrics are how you translate vague notions of "good enough" into concrete, measurable criteria.
Without clear success metrics, you can't know if your agent is improving, regressing, or ready for production. Metrics turn subjective judgments ("seems to work") into objective data ("succeeds 87% of the time"). They guide iteration, prevent regressions, and validate production readiness.
Interactive: Explore Metric Categories
Click each category to understand what to measure and why it's important:
The right success metrics depend on your task. For a research agent, accuracy matters more than speed. For a customer service agent, response time and user satisfaction are critical. For a code generation agent, correctness and security are paramount. Tailor metrics to your specific use case.