The short answer: Start with one repeatable task, a visible review step, and a clear definition of done. Add autonomy only when the simpler workflow cannot do the job.
An AI system does not need a dramatic job title to be useful. A tool that turns a messy client inquiry into a clean project brief can be more valuable than a sprawling assistant that promises to run the whole business.
The practical question is simple: what work should be easier by the end of the day? Start there. The architecture can follow.
A workflow has a route. An agent chooses more of the route.
In its engineering guide, Anthropic distinguishes predefined workflows from systems where a model directs its own process and tool use. It also recommends starting with simpler solutions before adding complexity. That is a useful design principle, even as particular products evolve. Read Anthropic’s distinction.
Imagine a contact form on a design studio’s website. A workflow can collect the submission, extract requirements into a fixed template, identify missing information, and save a draft reply for review. You know what happens next at every stage.
An agent might decide which past projects to inspect, which questions to research, and which tools to call. That flexibility can be useful. It also means you now need to understand the quality of the decisions between the input and the answer.
Choose a task with a small, useful finish line.
A good first candidate has recognizable inputs and an output someone already knows how to assess. Try summarizing a meeting, classifying support requests, or turning an approved outline into a first draft. Avoid starting with “handle marketing” or “manage clients.” Those are departments, not task definitions.
For the inquiry example, define the finish line as a brief containing the business, audience, requested pages, timeline, budget if provided, and unanswered questions. Missing information should stay missing. The system should not fill a blank budget with a plausible number.
Write a sample good result by hand. Include an awkward case, such as a message that only says, “I need a website soon.” This gives you a concrete way to judge the system instead of deciding whether the answer feels impressive.
Make the review step part of the product.
A review button is useful only when the reviewer can see what matters. Show the original inquiry beside the generated brief. Highlight uncertainty. Make editing straightforward. Keep the next action explicit: save, revise, or send.
For a first version, let AI prepare the reply while a person sends it. Drafting a proposal and committing to a price are different responsibilities. Your interface should make that boundary obvious.
This is a practical application of risk-aware design, rather than a claim that one approval step solves every problem. NIST’s voluntary AI Risk Management Framework describes trustworthiness as something to consider across design, use, and evaluation. The framework is a broader starting point.
Measure the whole job, including corrections.
A short generation time does not mean the workflow saves time. If a person spends ten minutes finding invented details in a two-minute draft, the result is expensive in a different place.
For a small pilot, record whether the brief captured the requested work, whether it invented any details, what the reviewer changed, and whether the result helped the next conversation. These are suggested evaluation questions, not reported results.
Collect failures as examples. A confusing response to a vague inquiry is often more useful than another polished response to a perfect one. Use those cases when comparing revisions to the prompt, model, or workflow.
Add autonomy to solve a demonstrated problem.
Perhaps inquiries vary enough that the system needs to select a different brief template. Add that bounded decision first. Perhaps it needs to look up a public company website. Add that tool with a visible source link and a clear limit on what may be retrieved.
Expand one responsibility at a time. Keep a way to stop, inspect, and recover. If the ordinary workflow already handles the job well, there is no architectural prize for making it more autonomous.
The best first AI project is often small enough to explain on a single page and useful enough that someone wants to use it again tomorrow.
Key takeaways
- Write down the task before choosing the technology.
- Separate suggestions from actions that change the outside world.
- Measure useful output, correction effort, and the quality of exceptions.
A few good questions
What is the difference between an AI agent and a workflow?
A workflow follows a sequence you define. An agent chooses more of its own steps and tool use. Products use these terms differently, so inspect the actual behavior.
When should a workflow become an agent?
When your examples show that fixed steps are the constraint, the task has clear boundaries, and you can observe and evaluate the additional decisions.