From AI Use Case to Production

Understand the work before the architecture

Observe users completing representative tasks. Capture inputs, system boundaries, exceptions, and the cost of mistakes. Establish what a successful result means and collect examples that can become an evaluation set.

Check whether AI is needed at all. A deterministic integration or a simpler workflow may solve the problem with less operational risk.

Prototype the smallest complete task

Choose one narrow path from input to useful outcome. Use retrieval-augmented generation when the task needs grounded knowledge, and tool calls when it needs to act on systems. Start with a simple architecture; introduce multiple agents only when separated responsibilities improve measurable performance.

MCP can standardize access to tools and context, but it does not replace authorization. Scope each tool to the minimum permissions required and validate inputs independently of model output.

Evaluate behavior, not just answers

Test normal cases, ambiguity, missing data, prompt injection, and unavailable dependencies. For an action-taking agent, verify the intended action, permission boundary, and final system state—not just the conversational response.

• Keep a versioned evaluation set with domain-reviewed expected outcomes.
• Track task success, correction rate, latency, and cost.
• Require approval for consequential actions and provide a manual fallback.
• Log enough context to investigate failures without exposing sensitive data.

Release progressively and keep learning

Start in shadow mode or with a small user group when practical. Name the service owner, document rollback, and establish monitoring before increasing usage. Pair product measures with business outcomes so local technical improvements do not hide a worsening workflow.

The production release is the start of operational learning. Changes to models, prompts, tools, and source data should trigger appropriate regression testing.

Put the idea to work.

Related concepts