Because final-answer accuracy metrics often conceal silent failures and model misalignment, evaluation methodologies are pivoting toward full-lifecycle process auditing and procedural conformance.
Evidence
- Evaluation Misalignment reveals that model priors shaped by standard training metrics often contradict expert values and cannot be blindly trusted in agent building.
- ContractEval addresses hidden failures by diagnosing procedural instruction conformance rather than just final state correctness.
- SearchAtlas demonstrates that analyzing agent behavior at the process level (via evidence graphs) exposes reasoning failures missed by end-to-end accuracy metrics.
- AgentAudit introduces a framework for evaluating the full execution pipeline (planning, memory, tool use) to pinpoint exact failure sources.
Implications
- Agent builders will need to implement step-by-step audit trails and execution tracking (similar to Docket) to verify production safety.
- Static question-answering benchmarks will be largely replaced by trajectory-based and generation-aware evaluations for complex AI systems.
Concepts
LLM Evals Agents RAG Evaluation
Confidence
high