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.
AI agents are often capable but unreliable over long horizons. Reliability captures and lets us measure the gap between capability and deployability.
