#evaluation
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Building Reliable AI Evaluation for High-Stakes Domains
Static rubrics fail to catch critical AI errors because they lack context. Instead, build a continuous loop: discover failure modes from real outputs, capture expert judgment, and calibrate each evaluation using case-specific context.
AI EngineerEngineering Clinical Intelligence at Scale
Abridge scales clinical documentation and decision support by treating evaluation as the core operating system, using human-calibrated LLM judges, and optimizing costs through task-specific model decomposition.
AI EngineerFixing Computer Use Benchmarks: Beyond Replay Exploits
Current computer use benchmarks are often gamed by 'replay agents' that blindly repeat successful trajectories. Robust evaluation requires stochastic, verified environments and honest statistical uncertainty to avoid costly deployment errors.
AI EngineerRaising the Floor: Practical AI Agent Evaluation
Stop chasing benchmark scores and start treating agent evaluations like production software tests. Focus on identifying when issues start and their impact on user volume to build reliable, trust-based AI products.
AI EngineerSecuring AI Evaluation Environments Against Model Misbehavior
As AI models become more capable, third-party evaluation environments require stricter security controls to prevent models from escaping simulated boundaries and interacting with the real internet.
The Benchmaxxing Plague: Why AI Benchmarks Fail Reality
Benchmarks are increasingly gamed by labs to inflate performance scores, leading to a disconnect between leaderboard rankings and real-world utility. The solution requires moving away from automated, synthetic metrics toward high-fidelity human evaluation and domain-expert curation.
AI EngineerEvaluating AI Agents in Real-World Environments
Static benchmarks are insufficient for long-horizon AI agents. Andon Labs uses real-world deployments (cafés, retail stores, radio) and environment-forking simulations to measure emergent behaviors like collusion, power-seeking, and safety failures.
AI EngineerGraphContainer: A Unified Platform for Graph RAG Evaluation
GraphContainer is a platform designed to standardize the comparison and debugging of Graph RAG pipelines, addressing the lack of unified tooling for evaluating graph-based retrieval methods.
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 13 of 13