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AI model evaluation usually ends the day a workload goes live. This guide works through the questions you have to answer before you commit a model to production. It shows how to evaluate AI models on your own workload and what each useful result costs at your expected volume, so your enterprise AI model selection rests on evidence you gathered yourself.

An enterprise AI agent stack is only as governable as the shared layer underneath its agents. When 20 teams build agents separately, you get 20 paths into your systems, each with its own credentials and tool connections. Controlling agent sprawl comes down to deciding what moves into that shared layer and what stays with the teams building agents.

This is what AI technical debt looks like in an enterprise with 15 applications in production. The code in each application may be clean. The debt comes from building the same capability many times, in different ways, by different teams. Now each version has to be maintained on its own.

AI agent reliability is decided by the inputs your team never tested. Your evaluation score only covers the inputs you chose, and the untested ones arrive in the first week of production.

The AI question that matters once dozens of use cases are live shifts from what to build next to what deserves continued engineering capacity and budget.

Enterprise AI gets harder to manage as applications, agents, models, data, and infrastructure multiply across teams.

An AI-native SDLC is a software development lifecycle where AI's autonomy is explicitly defined at each stage and the workflow is rebuilt around that definition.