Setting Goals and Success Criteria › Glossary
AI Agent HandbookAll chapters Setting Goals and Success Criteria › GlossaryOne section · Sections in this chapter12 / 13 ↓
Correctness: A measure of whether an agent produces accurate answers or successfully completes requested tasks. This is the most fundamental evaluation criterion.
Efficiency Criteria: Standards that measure how quickly an agent responds and how many resources (tool calls, API requests, compute time) it uses. Efficiency matters for both user experience and cost.
Reliability: A measure of how consistently an agent performs across different phrasings, contexts, and edge cases. A reliable agent gives correct answers predictably, not just occasionally.
Safety Criteria: Standards ensuring an agent refuses inappropriate requests, protects sensitive information, and operates within defined boundaries. For many applications, safety criteria should have no tolerance for failure.
Success Criteria: Specific, measurable standards that define what counts as successful agent performance. They transform vague goals like "works well" into concrete targets like "answers correctly 90% of the time."
Threshold: The minimum acceptable value for a success criterion. For example, "response time under 3 seconds" or "correctness above 85%." Thresholds make success criteria actionable.
User Experience (UX) Criteria: Standards that measure qualitative aspects of agent interactions, such as clarity, tone, and helpfulness. These criteria often require user feedback to evaluate.