Researchagentsevaluation metricshallucinations
Agent Developers Rethink Evaluation Using Trajectories
7.0
An AI agent developer who has built agents for the past year warns that most teams evaluate agents solely by final answers, which misses intermediate failures. They experimented with using the system prompt as ground truth, scoring full execution trajectories with multidimensional metrics, and found it reveals hallucinations, constraint violations, and inefficiencies previously hidden.
Key Points
- 1Highlight trajectory-based evaluation: measure full agent steps against system prompt ground truth, not just outputs
- 2Reveal failures: detects hallucinations, constraint violations, inefficiencies that single-output metrics miss
- 3Advise practitioners to adopt multidimensional scoring and trajectory audits to improve agent reliability
Scoring Rationale
Practical, actionable methodology with clear benefits; limited by anecdotal evidence and lack of formal benchmarks.
Sources
Public references used for this report.
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