Evals: What Every AI Engineer Should Know in 2026
Summary
This article explains why AI evals are becoming a core discipline for building reliable AI systems in 2026. It covers offline, online, human, and execution-based evaluation methods, along with red-teaming and benchmark design for agents, retrieval, tools, memory, routing, and safety. The piece argues that evals should move into production workflows like CI/CD and release gates so teams can catch regressions and measure real-world performance. It also highlights vendor-relevant needs such as reusable evaluation harnesses, benchmark datasets, LLM-as-judge workflows, and safety testing infrastructure.
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