Evaluation
Reliability (AI agents)
2025ActiveUpdated: 15 September 2026Published
Key
innovation
Framing an AI agent's ability to act consistently, repeatably and predictably over a long horizon as a measurable property.
Category
Evaluation
Abstraction level
Pattern
Operation level
SystemAgent runtimeEvaluation (runtime)
Use cases
AI agent evaluationDeploying autonomous agentsLong-horizon benchmarks
How it works
Reliability is assessed through repeated runs in realistic, multi-step scenarios, measuring output consistency, resistance to error accumulation and drift, and behavior in edge cases. It is improved by better planning, verification of intermediate steps, error-correction mechanisms and oversight.
Problem solved
AI agents are often capable but unreliable over long horizons. Reliability captures and lets us measure the gap between capability and deployability.