A workflow follows a known sequence of states and rules. An agent chooses some steps dynamically based on a task and tool results. Businesses often need both: language understanding for an unstructured request, and explicit application rules for money, permissions, and final state changes.
A practical scenario
A purchase-request process illustrates the distinction. A model can interpret a message and propose a category. A rules engine should check the budget, approver, and permitted supplier. An agent may investigate missing information, but it should not invent approval authority or decide that a failed payment succeeded.
Design the first version
Draw a state diagram before adding autonomy. Define which tools are read-only, which create drafts, and which perform final actions. Set limits for retries, total steps, and spending. Store the task state outside the model conversation so an interrupted run can resume without repeating an irreversible operation.
What to test and measure
Evaluate completion accuracy and unintended actions separately. Test a tool timeout, contradictory records, malicious content in a document, and an instruction that conflicts with policy. If the process rarely needs dynamic planning, prefer a workflow with a narrow model step. Flexibility is useful only when its extra complexity solves a real operational problem.
Questions to resolve before commissioning
- Which source system owns the facts used in this workflow?
- Who reviews exceptions and corrects inaccurate output?
- What baseline and acceptance criteria will determine whether the pilot is useful?
- What should the user do when a source, tool, or device is unavailable?
Explore the implementation
This is a planning guide, not a report of measured client results. Examples are illustrative. Explore the related SyntaxLab demo to discuss the interaction, then use your own records and acceptance criteria for a production pilot. Discuss a scoped project or review our AI automation services.
Further reading
Read OWASP guidance on excessive agency for technical background. Continue with From AI Pilot to Production: A Business Launch Checklist.