Choose a bounded task
Start with work that is repetitive, understood and measurable. Document classification or extracting a small set of fields may be easier to evaluate than an assistant expected to handle every question in the business.
Map the inputs, decisions and downstream actions. If the workflow is unclear to a person, introducing a model will not resolve that ambiguity.
Build an evaluation set
Collect representative examples, including ambiguous and malformed inputs. Define what a useful answer looks like and which errors are unacceptable. Keep sensitive information out of test data unless appropriate controls are in place.
Compare the AI-assisted workflow with the current process. Consider review time, correction effort and cost per completed task alongside output quality.
Keep authority where it belongs
Model output should not automatically acquire permission to act. Validate structured outputs, limit available tools and require approval for consequential changes. Documents and retrieved text are untrusted inputs, not instructions to the system.
Human review works best when reviewers can see the source, the proposed action and the uncertainty. A generic approval button is rarely enough.
Plan for operations
Record the model and prompt versions used for evaluation. Set a budget, define retention rules and monitor failures. A provider change or a new document type can affect quality even when application code stays the same.
Expand only when the bounded workflow is useful and its failure modes are understood. The value comes from dependable work completed, not the size of the agent architecture.
These field notes describe general product and engineering considerations. The right approach depends on the requirements and constraints of your project.