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Agent evals are drifting away from production reality. Most benchmarks use clean tasks, well-specified requirements, deterministic metrics, and retrospective curation. Production work is messier, with implicit constraints, fragmented multimodal inputs, undeclared domain knowledge, long-horizon deliverables, and expert judgment that evolves over time. This paper introduces AlphaEval, a production-grounded benchmark for evaluating agents as complete products. AlphaEval contains 94 tasks sourced from seven companies deploying AI agents in core business workflows, spanning six O*NET domains. It evaluates systems like Claude Code and Codex as commercial agent products, not just model APIs. The benchmark combines multiple evaluation paradigms: LLM-as-a-Judge, reference-driven metrics, formal verification, rubric-based assessment, automated UI testing, and domain-specific checks. Why it matters: organizations need benchmarks that start from real production requirements, then become executable evals with minimal friction. Paper: https://t.co/cbTGgTWoNl Learn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c

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