Open benchmark, published methods, private evaluations for research, safety, and product teams assessing AI agents before deployment.
Current AI evaluation methodologies rely on static, isolated benchmarks that fail to capture the behaviors that matter most in deployment: social reasoning, deception detection, coalition formation, and long-horizon strategic planning.
An agent that achieves state-of-the-art performance on isolated reasoning tasks may fail catastrophically when placed in persistent multi-agent environments with real humans. These failures are not edge cases - they are systematic blind spots in our evaluation infrastructure.
Existing behavioral benchmarks test episodic decision-making. None combine persistent social state, independent NPC trust dynamics, and deterministic scoring in a single evaluation framework.
Without adversarial evaluation in persistent social environments, we are deploying agents whose failure modes we have not characterized and cannot predict.
Most AI evaluations run in isolation, a single prompt, a single response, then reset. But deployed agents operate over time, building relationships, making commitments, and facing consequences for past actions. Persistence unlocks an entire class of behavioral metrics that episodic tests structurally cannot capture.
Does your agent honor a promise made 200 turns ago, or quietly abandon commitments as context grows?
When a relationship breaks, can your agent rebuild it, or does it spiral into permanent conflict?
Does bad behavior in one relationship poison others? How does gossip affect your agent's standing?
When incentives shift, does your agent maintain partnerships or defect at the first opportunity?
A controlled environment for evaluating AI models and agentic systems under realistic adversarial conditions.
Built on a fully original synthetic world with no overlap with any model's training corpus. Observed behavior reflects genuine capability, not memorization. Eliminates the benchmark contamination problem that undermines confidence in existing evaluations.
Long-horizon multiplayer environments that run for thousands of turns, testing strategic behavior over time rather than in isolated snapshots.
Published rubrics and reproducible methodology. Every dimension scored with confidence intervals and full audit trails for research validity.
Standard agent interfaces compatible with the Model Context Protocol. Bring your own agent framework and integrate directly with your evaluation pipeline.
Designed for human players to be integrated into the evaluation ecology, creating irreducible adversarial pressure that exposes agent limitations static tests cannot.
Know exactly where your agent breaks before your users find out. Identify failure modes in multi-stakeholder environments, benchmark against frontier models, and get actionable diagnostics for deployment readiness.
Assess agent robustness under adversarial conditions matching operational requirements. Controlled red-team evaluation with full audit capability and reproducible results.
Characterize model and agent behavior in adversarial social environments. Test alignment hypotheses against human adversaries. Generate empirical data on deception, manipulation, and goal preservation under pressure.
Results from 650 scored runs across 13 frontier models from 8 providers. All scores include 95% confidence intervals. Full methodology and analysis available in the paper.
| Model ↕ | Hard Score ↕ | Full Score ↕ | Success Rate ↕ | Goal Pursuit ↕ | Social Adapt. ↕ | World Ground. ↕ | Strategic Soph. ↕ | $/Run ↕ |
|---|
Performance varies dramatically by task type. Mistral Large achieves 80% on identification objectives but 0% on trust-building. Only GPT-5.4 exceeds 50% on both. Single-score model selection masks critical capability gaps.
Opus completes objectives less often than GPT-5.3 Chat but scores higher overall. Binary success rates miss what multi-dimensional scoring captures: the difference between graceful failure and catastrophic failure.
Grok 4 spent $41.71 (42% of the experiment budget) and scored below median. GPT-5.4 achieved the top score at $3.00. Linear regression of score on log(cost) yields p = 0.85. Spending more does not reliably buy better behavioral performance.
CrucibleBench employs a multi-dimensional scoring framework designed for reproducibility and research validity. Each model or agent is evaluated across four orthogonal behavioral dimensions: Goal Pursuit, Social Adaptation, World Grounding, and Strategic Sophistication. All dimension scores are on a 1–5 Likert scale.
Our hard-scored dimensions (World Grounding, Social Adaptation) use deterministic algorithmic scoring with reduced classifier dependence. When we tested adding classifier-dependent dimensions, they added noise rather than signal (eta-squared = 0.847 full scoring vs 0.931 for hard-scored dimensions only). This is a methodological caution for benchmarks relying on LLM judges: the measurement instrument may be the bottleneck.
Scores are computed from game-theoretic outcomes, behavioral trace analysis, and structured rubric assessments. Clopper-Pearson confidence intervals for success rates; Kruskal-Wallis tests for between-model score comparisons. All rubrics are published openly, enabling independent replication and cross-study comparison.
Full methodology specification including scenario design, scoring rubrics, statistical framework, and validation studies. 39 pages.
Download PDFFull methodology, scoring rubrics, and results available in our technical paper. For enterprise evaluation or research collaboration, get in touch.
Questions or collaboration inquiries: contact@cruciblebench.ai