[{"data":1,"prerenderedAt":157},["ShallowReactive",2],{"blog-post-blog_en-ki-agenten-evaluierung-fuer-produktive-systeme":3},{"id":4,"title":5,"body":6,"cover":141,"date":142,"description":143,"draft":144,"extension":145,"meta":146,"navigation":147,"path":148,"seo":149,"stem":150,"tags":151,"__hash__":156},"blog_en\u002Fen\u002Fblog\u002Fki-agenten-evaluierung-fuer-produktive-systeme.md","AI Agent Evaluation: Making Quality Measurable Before Production",{"type":7,"value":8,"toc":134},"minimark",[9,13,18,21,24,53,56,67,71,74,77,109,112,115,119,122,125],[10,11,12],"p",{},"AI agent evaluation becomes essential once an agent does more than generate text and starts reading data, calling tools, or changing business processes. Conventional tests still validate APIs and deterministic logic. They do not show whether an agent reliably reaches the right goal despite variable outputs.",[14,15,17],"h2",{"id":16},"what-ai-agent-evaluation-must-measure","What AI Agent Evaluation Must Measure",[10,19,20],{},"A useful eval does not broadly judge whether an answer sounds good. It breaks the workflow into verifiable quality criteria that Product, Engineering, and domain experts define together.",[10,22,23],{},"Four levels matter for production agents:",[25,26,27,35,41,47],"ul",{},[28,29,30,34],"li",{},[31,32,33],"strong",{},"Outcome:"," Is the response factually correct, complete, and in the agreed format?",[28,36,37,40],{},[31,38,39],{},"Tool use:"," Does the agent select the right tool, pass valid parameters, and respect its permissions?",[28,42,43,46],{},[31,44,45],{},"Trajectory:"," Does the agent reach the goal in a reasonable number of steps without loops or unnecessary model calls?",[28,48,49,52],{},[31,50,51],{},"Business impact:"," Is a support case resolved correctly, a risk identified, or a human handover triggered in time?",[10,54,55],{},"OpenAI Agent Evals, Google Cloud's Gen AI evaluation service, and LangSmith now treat these checks as a distinct part of the development lifecycle. Tool choice is secondary, however. What matters is a domain-specific definition of success that remains valid across models and providers.",[10,57,58,59,62,63,66],{},"One aggregate score is rarely sufficient. An agent can sound convincing while updating the wrong customer account. ",[31,60,61],{},"Hard criteria"," such as schemas, permitted tool calls, and required fields should therefore be assessed separately from ",[31,64,65],{},"soft criteria"," such as clarity or tone.",[14,68,70],{"id":69},"where-teams-should-start-with-evals","Where Teams Should Start With Evals",[10,72,73],{},"The most common mistake is building a large synthetic benchmark with little connection to real workflows. A better starting point is 20 to 30 carefully reviewed cases from the actual process: common tasks, expensive failures, edge cases, and situations in which the agent must stop.",[10,75,76],{},"A small, reliable evaluation process can then be built around them:",[25,78,79,85,91,97,103],{},[28,80,81,84],{},[31,82,83],{},"Curate reference cases:"," Domain owners provide inputs, expected outcomes, and prohibited actions.",[28,86,87,90],{},[31,88,89],{},"Use deterministic checks:"," JSON schemas, tool parameters, permissions, and business rules are tested with conventional code.",[28,92,93,96],{},[31,94,95],{},"Calibrate judgements:"," LLM-as-a-judge can assess semantic quality, but its scores need regular comparison with human review.",[28,98,99,102],{},[31,100,101],{},"Compare versions:"," Prompt, model, retrieval, and tool changes run against the same dataset.",[28,104,105,108],{},[31,106,107],{},"Set release gates:"," Critical security or process failures block a release even when the average score improves.",[10,110,111],{},"Production failures should return to the dataset as regression tests. This grows coverage through real usage rather than invented variations. Online evaluation and user feedback complement this process, but do not replace assessment before release.",[10,113,114],{},"Ownership matters more than the platform. Product leaders define value, domain experts assess edge cases, and Engineering automates execution. Without that division, a team can easily measure technical elegance instead of business success.",[14,116,118],{"id":117},"why-this-matters","Why This Matters",[10,120,121],{},"AI agents can change after a new model, an edited prompt, or a different data source even when no conventional test fails. Without AI agent evaluation, these regressions surface only through complaints, incorrect actions, or rising operating costs.",[10,123,124],{},"Reliable evals shorten model migrations, make release decisions defensible, and limit the risk of increasing autonomy. They also help decision-makers consider quality and cost together instead of comparing only demo results or token prices.",[10,126,127,128,133],{},"For growing software companies, evaluation is therefore not a downstream quality step but part of product architecture. An ",[129,130,132],"a",{"href":131},"\u002Fen\u002F#packages","Architecture & AI Review"," can clarify which agent workflows need measurable criteria, regression tests, and binding release thresholds.",{"title":135,"searchDepth":136,"depth":136,"links":137},"",2,[138,139,140],{"id":16,"depth":136,"text":17},{"id":69,"depth":136,"text":70},{"id":117,"depth":136,"text":118},null,"2026-06-27","AI agent evaluation makes quality, tool use and risk measurable before release. How teams build reliable evals for production systems.",false,"md",{},true,"\u002Fen\u002Fblog\u002Fki-agenten-evaluierung-fuer-produktive-systeme",{"title":5,"description":143},"en\u002Fblog\u002Fki-agenten-evaluierung-fuer-produktive-systeme",[152,153,154,155],"AI","Software Quality","Engineering Leadership","Software Architecture","x_2d9qC9mTywRFySVVbY5zLbJT4NtW63NEXF_-I8Vcs",1783430348451]