Agentic workflow examples: from request to resolution

What makes a workflow agentic?

An agentic workflow uses AI to interpret a task or decide how to proceed within a larger business process. Some steps can follow fixed rules; others may let an agent select a tool or investigate an exception. “Agentic” does not require every step to be autonomous.

Anthropic’s engineering guide distinguishes predefined workflows from agents that dynamically choose their process and tools. When reviewing a design, ask which decisions belong to code, which belong to the model and which belong to a person.

The following examples are illustrative designs. They show how we would scope a workflow, including the cases that need a human, rather than report results from a client deployment.

Example 1: a booking change

A customer asks to move an appointment and add another attendee. The desired outcome is a correctly recorded change or a clear answer about why it cannot be made.

A read-only API stops the automated path at preparation. The operator needs a review screen with the original request, the proposed change and a link to the source booking. Giving the model a different instruction cannot create a write capability the platform does not offer.

This is the boundary between generating a response and completing work. Define that boundary before estimating the project.

Example 2: a sales inquiry with existing history

A new website inquiry may be from someone the business already knows. Before creating another CRM contact, the workflow searches for a suitable match and brings forward relevant account history. If the identity is uncertain, it offers possible records for review.

AI can extract the request, identify the missing information and prepare a useful handoff. Ownership, territory and routing rules can remain explicit application logic. The resulting task should include the source message and enough context for the assigned person to act.

Our first checks would be a repeat customer using a new address, two people sharing an office number and an inquiry with no clear location. Those examples test the customer matching and data structure beneath the workflow.

Completion means the inquiry is attached to the right record and has a responsible owner. A generated lead summary alone does not demonstrate either.

Example 3: an operations exception

An order reaches the dispatch queue without a delivery date. A workflow can look up the order, inspect the supplier update and determine what information is missing. It might prepare a request for clarification or propose a revised task for an operator to approve.

Keep the financial and operational boundaries specific. For this example, the agent can draft a supplier message and add an internal note. It cannot approve a new purchase, change the delivery commitment or silently mark the order complete.

The useful interface is a short queue of unresolved cases, each with the evidence, a proposed next step and the person responsible. That may require a small custom operations application as well as connections to the order system.

Build the interrupted path too

A connection can fail after a platform accepts an update but before your application receives the response. Retrying without checking can create duplicate records or actions. Stripe’s idempotency documentation gives a concrete example of using request keys to retry supported operations without repeating them. Other systems need their own documented recovery behavior.

For each integration, decide how a worker checks an uncertain result. Preserve the source request, the action attempted and the last confirmed state. A timeout should appear as unresolved until the system can establish what happened.

• A missing record goes to a named review queue.
• An expired connection creates an alert with an owner.
• A changed business rule blocks the affected action until checked.
• A disputed result can be traced back to its inputs and recorded changes.

Measure completed work and corrections

For the booking workflow, count confirmed changes, exceptions needing review, duplicate attempts and corrections after release. For the sales handoff, inspect record matches and owner assignment. The measurements should reflect the job the team is trying to finish.

Keep failed examples and rerun them when a prompt, tool or mapping changes. A corrected customer match or an interrupted update should become a repeatable test. That is part of our release and improvement process.

If one of these handoffs resembles your operation, our agentic AI and automation work starts by tracing an actual request across the tools involved. We can look at that request together.