AI Evaluation

Microsoft ASSERT is an open-source agent evaluation framework that turns written AI policies, product requirements, and safety rules into executable tests. For developers, the value is practical: instead of debating whether an agent “mostly follows policy,” ASSERT gives you repeatable scenarios, metrics, traces, and scorecards you can run before release. What Is the Microsoft ASSERT Agent Evaluation Framework? Microsoft ASSERT is a requirement-driven evaluation harness for AI agents and LLM applications that converts natural-language specifications into executable evaluations. ASSERT stands for Adaptive Spec-driven Scoring for Evaluation and Regression Testing, and Microsoft describes it as open source and framework-agnostic for the estimated 6 million to 13 million generative AI developers working across today’s agent ecosystem. The framework starts with written intent, such as a product requirement, policy document, system prompt, or launch checklist, then helps generate scenarios, datasets, metrics, and scorecards that can be run against hosted models, Python callables, or traced agent systems. The key idea is simple: agent behavior should be tested against your own requirements, not only against generic benchmarks. ASSERT is best understood as policy-as-evaluation for teams that need repeatable evidence before deploying autonomous workflows. ...

Claude Opus 4.6 resolves 80.8% of real GitHub issues on SWE-bench Verified while GPT-5.5 leads Terminal-Bench 2.0 at 82.7% — numbers that mean something precisely because they come from independent evaluation pipelines, not vendor press releases. Choosing an LLM in 2026 without understanding how these benchmarks work is like buying a server based solely on manufacturer marketing sheets. This guide covers the LM Council evaluation framework, the top independent leaderboards developers actually rely on, and how to read benchmark results without getting misled. ...

LLM benchmark scores flood every model release announcement — but as of 2026, most of those scores tell you almost nothing useful. This guide explains which benchmarks still matter for developers, which are saturated or compromised, and how to pick the right signal for your actual workload. Why LLM Benchmarks Matter for Developers (And Why Most Are Now Useless) LLM benchmarks are standardized test suites that measure model capabilities across defined tasks — coding, reasoning, math, or domain knowledge — so developers can compare models without running every candidate through their own production workload. Done right, they save weeks of internal evaluation. Done wrong, they create a false confidence loop where a model scores 92% on a benchmark and then fails on the first real customer ticket you throw at it. As of May 2026, the benchmark landscape has split sharply: a small set of hard, contamination-resistant evaluations still provide genuine signal, while the legacy suites — MMLU, HumanEval, GSM8K — have been effectively retired by the community because frontier models have saturated them. MMLU, once the canonical academic reasoning suite, now sees frontier models cluster at 85–90% with no meaningful spread between Claude, GPT, and Gemini variants. HumanEval similarly sees 93%+ scores across top-tier models as of April 2026. When every serious model aces the same test, the test stops being useful. The benchmarks worth tracking now are the ones that are still hard enough to differentiate — and that requires understanding why they’re hard. ...