The AI Accuracy Dilemma: An Evaluation Framework for AI Agents

info@fuzebox.ai (Nasim Binesh, PhD) — Wed, 23 Sep 2026 19:00:11 GMT

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

Cite this paper (APA 7)

Binesh, N. (2026). The AI accuracy dilemma: An evaluation framework for AI agents [White paper]. FuzeBox AI. https://fuzebox.ai/research/ai-accuracy-dilemma

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The Economic Ledger: A Preliminary Framework for Measuring the Net Productive Capacity of Human–AI Collaboration

info@fuzebox.ai (Nasim Binesh, PhD) — Wed, 23 Sep 2026 15:02:58 GMT

Abstract

The question of how much productive value is gained through human–AI collaboration is both theoretical and practical and remains under-researched. Existing approaches either measure theoretical task exposure (what AI could affect) without measuring realized enterprise value, or report gross productivity gains without subtracting the labor costs of review, rework, and oversight that those gains generate. This white paper proposes the Economic Ledger (EL) — the measurement of work in an environment where AI is introduced — as a candidate higher-order latent construct that bridges this gap: a net, task-grounded measure of productive capacity unlocked through human–AI collaboration, conditioned on task fit, adoption quality, and organizational readiness, and net of verification burden. We develop its theoretical and empirical foundations across three research streams: (1) AI Potential, Readiness, and Exposure, which establishes the determinant layer; (2) Work, Task Productivity, and Human/Agent Performance, which establishes the state and outcome layers; and (3) Entangled Workflows and Networked Productivity, which establishes the workflow- and team-level aggregation layer. Drawing on more than thirty primary sources spanning field experiments, randomized controlled trials, psychometric instrument validation, process mining, and enterprise survey research, we propose a preliminary measurement architecture suitable for empirical testing with enterprise data. Key contributions include: a three-layer determinant–state–outcome separation as the non-negotiable structural principle for defensible measurement; a systematic treatment of verification burden as first-class labor rather than residual friction; and the integration of agentic workflow provenance as an emerging infrastructure requirement for enterprise-grade traceability.

Keywords: Economic Ledger, human–AI collaboration, productivity measurement, verification burden, task exposure, organizational readiness, workflow coherence, agentic workflows, SEM, EFA/CFA

Inside the full paper

Executive Summary
1. Introduction: Defining the Economic Ledger
2. Structural Principles: Determinants, State, and Outcomes
3–5. Three Research Streams: Exposure, Productivity, and Entangled Workflows
6. Toward a Measurement Architecture
7. Implications
References (44 sources)

Cite this paper (APA 7)

Binesh, N. (2026). The Economic Ledger: A preliminary framework for measuring the net productive capacity of human–AI collaboration [White paper]. FuzeBox AI. https://fuzebox.ai/research/economic-ledger

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Research

The AI Accuracy Dilemma: An Evaluation Framework for AI Agents

Abstract

The Economic Ledger: A Preliminary Framework for Measuring the Net Productive Capacity of Human–AI Collaboration

Abstract