The AI Accuracy Dilemma: An Evaluation Framework for AI Agents

Abstract

The main problem in measuring AI system accuracy is that accuracy is not a single number. For a classifier it may mean correct labels; for a generator, factual faithfulness or preference alignment; for a tool-using agent, a combination of task completion, safe action choice, policy conformance, correct state transitions, and net economic value (Chouldechova et al., 2024; Wallach et al., 2024). Once AI systems act, accuracy is no longer a question about the model alone. It becomes a question about the whole system the model sits in—the tools it can call, the policies it must follow, and the steps it takes to get to an answer. The question is no longer only whether the model was right, but whether the system as a whole produced the right outcome, by the right procedure, under the right constraints, with defensible evidence. The state of practice is not yet ready for that question. A construct-validity audit of 445 LLM benchmarks found that only 16% report any statistical uncertainty (Bean et al., 2025); psychometric analyses show that some benchmark items have negative discrimination and actively distort rankings (P. Li et al., 2025); no agent evaluated on a major safety benchmark exceeded a 60% safety score (Zhang et al., 2024); agents complete only 27–48% of long-horizon procedural tasks (Z. Li et al., 2025; Nandi et al., 2025); and a substantial share of reported agent “successes” are procedurally corrupt—the task got done, but by breaking the rules (Cao et al., 2026). A benchmark score, on its own, is an anecdote.

Keywords: AI accuracy, AI agents, AI evaluation, construct validity, LLM-as-a-judge, policy compliance, runtime guardrails, human-in-the-loop evaluation

Inside the full paper

1. 1. Executive Summary
2. 2. Why AI Accuracy Is Not a Single Number
3. 3. Accuracy for Enterprise Agents
4. 4. The Proposed Evaluation System
5. 5. What Can Be Automated Versus What Requires Human Judgment
6. 6. Recommended Implementation Roadmap
7. 7. Final Remarks
8. References (57 sources)
9. Appendices A–C: Benchmarks, Metric Definitions, and Example Rubrics