#evaluation
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Beyond Accuracy: Evaluating AI Agents After Benchmark Saturation
When AI benchmarks saturate, accuracy becomes a poor metric. Researchers should instead evaluate agents across six dimensions: construct validity, generalizability, efficiency, reliability, model/scaffold performance, and human-agent collaboration.
Stress-Testing AI Agents with Simulated Digital Worlds
Patronus AI is moving beyond static benchmarks by using 'digital world models' to simulate complex environments, allowing developers to stress-test autonomous AI agents through reinforcement learning without human intervention.
Predicting AI Model Behavior via Deployment Simulation
OpenAI uses 'Deployment Simulation'—replaying real, de-identified user conversations with new models—to predict safety risks and undesired behaviors before public release, outperforming traditional synthetic evaluations.
Practical Evaluation Strategies for AI Agents
Benchmark numbers are not gospel, but they are essential for iterative improvement. Use them to hill-climb your agent's performance by identifying failure patterns rather than chasing leaderboard scores.
AI EngineerThe Art & Science of Benchmarking AI Agents
Effective AI benchmarks are not just snapshots of current performance; they are strategic tools that define future capabilities, require rigorous task quality, and prioritize researcher UX to drive field-wide progress.
AI EngineerShowing 5 of 5