Explore/benchmark/Measurement Without Validity: The Compounding Reliability Problem in Agentic AI Evaluation
M

William Caban/Measurement Without Validity: The Compounding Reliability Problem in Agentic AI EvaluationUnknown

Agentic AI systems are evaluated using automated benchmarks whose scores justify deployment decisions, safety certifications, and regulatory compliance claims. We present an empirical analysis demonstrating that these scores are systematically less trustworthy than current practice acknowledges. The problem operates at three compounding layers. First, tasks are increasingly generated by language models: audits of ten popular benchmarks found validity flaws in seven and reporting gaps in all ten. Second, human users are replaced by LLM simulators, but calibration studies document inter-simulator variance up to 9 percentage points and systematic directional miscalibration, particularly for non-Standard American English speakers. Third, our structured survey of 55 papers finds that approximately 82% apply structurally mismatched, incomplete, or absent inter-rater reliability (IRR) metrics. These failures compound multiplicatively rather than additively. Under independence, a pipeline retaining 70% of valid signal at task generation, 80% at simulation, and 65% at judgment is at most 36% valid against the intended construct; the bound spans 0.22--0.54 across the empirical estimate range. We formalize this as $V_{\text{total}} \leq V_1 \times V_2 \times V_3$ and show it tightens further under correlated failures when the same model family operates across all three layers. We derive eight prescriptions grounded in psychometric science: a simulation calibration floor of $\text{ICC}(A,1) \geq 0.70$; domain-stratified reliability thresholds ($α\geq 0.67$ / $0.70$ / $0.80$ by consequence level); structured IRR metric selection rules based on pipeline design; and IRR as a mandatory reporting field. The measurement tools exist; the field's task is to apply them.

benchmark
GitHubCompare
Refreshed 21h ago
OverviewActivity52wAlternativesDocs
Stars0
Forks0
HF Downloads—30d
Last commit—
Refreshed21h ago
Project healthUnknownNo activity data.
Production readinessResearch / EarlyBest for exploration and prototyping.
Risk notesUnknown licenseVerify license before production use.
AgentHub Score
55 / 100
Composite score from 6 signals. How we score →
Active project
55Score
Growth
40C
Activity
30C
Documentation
70C+
Maturity
45C
Community
42C
Production
58C
GitHub stars · 0 days observed0 not enough history
snapshots
not enough history
Repository activity · 0 days observednot enough history from pushed_at
inactivepushed
not enough history
not enough history
Practical assessment
Should you use it?

✓ Best for

  • Research and experimentation
  • Prototype development
  • Learning agentic patterns

◎ Strengths

  • Active community
  • Open source
  • Well-documented API

✕ Not ideal for

  • Untested at scale without validation
  • Teams without AI/ML expertise

⚠ Watch-outs

  • Review changelog before updating
  • Verify license for commercial use
Technical details
What's inside
Language—
License—
Sourcearxiv
Open source✗ No
Commercial use—
Docs—
Demo—

AgentHub Score

55
Score 55/100
Below average

Alternatives

A
AgentBench
3.6k · benchmark
61
W
WebArena
1.6k · benchmark
61
V
VisualAgentBench
274 · benchmark
60
A
ALFWorld
819 · benchmark
55Compare all →

Recent activity

Latest commit ——
Indexed by AgentHub crawler21h ago
Monitor for new releasesongoing