Start with the job, not the model
The strongest AI agent opportunities begin with a specific piece of work. That work has a trigger, information it depends on, decisions it can make, and a clear point where responsibility returns to a person. Starting with a model or a chat interface often hides those practical details.
Write the job as a short operational sentence: when this event happens, use these sources to prepare or complete this action for this person. If that sentence stays vague, the agent is not ready to scope.
- Name the event that starts the workflow.
- List the systems and information the agent may use.
- Define the action it is allowed to take.
- Identify the person responsible for the final outcome.
Map boundaries before autonomy
Autonomy is not an all-or-nothing decision. An agent can collect context, draft a response, recommend an action, or complete an action. Each level carries a different operational and reputational risk.
A useful first release usually gives the system enough freedom to remove repetitive work while keeping consequential decisions visible. Confidence thresholds, approval steps, audit history, and explicit fallbacks should be part of the product design rather than late safety additions.
The right question is not “Can the agent do this?” It is “What should happen when the agent is uncertain?”
Design the evaluation with the workflow
A demo can look convincing while failing the cases that matter in daily work. Before implementation, assemble a small evaluation set from representative inputs: ordinary requests, ambiguous requests, missing information, conflicting information, and cases that must be escalated.
The evaluation should measure the outcome people care about. Depending on the workflow, that may be a correct classification, a complete draft, a traceable recommendation, or a safe refusal. Model output quality is only one part of system quality.
- Test common, edge, and intentionally unsafe inputs.
- Record why a result passed or failed.
- Review latency, cost, and human correction effort together.
- Keep the evaluation set as the workflow changes.
Ship the smallest complete loop
The smallest useful release is not the fewest screens. It is the narrowest version of the workflow that can run from trigger to accountable outcome. That often includes an interface for review, activity history, permissions, and a way to correct or retry work.
Once the loop works, expansion becomes an evidence-based decision. Add new actions, sources, or autonomy only when the existing workflow shows where more leverage is available.
