A customer asks for a delivery date. Someone checks a spreadsheet, messages the warehouse, searches an email thread, and calls the account manager. Twenty minutes later, the customer gets an answer.
It looks like an ideal task for an AI agent: gather the information, coordinate the steps, and respond automatically.
But what if the spreadsheet is outdated? What if the warehouse and sales team use different definitions of “confirmed”? What if nobody knows who can approve a revised delivery commitment?
An agent might navigate those systems quickly. It still needs a reliable way to determine which information is correct and what it is allowed to promise.
Before building an AI agent, businesses need to understand the workflow it will enter. Otherwise, automation can turn an operational problem into a recurring one.
Start with the outcome
“We need an AI agent” describes a proposed solution. It does not explain the business problem.
A better starting point is specific: reduce the time needed to answer delivery questions, shorten invoice processing, or help support staff resolve routine requests without searching five systems.
That outcome gives the project boundaries. It identifies the people involved, the information required, and the result that would make the investment worthwhile.
It also helps determine whether an agent is necessary. A system integration, a clearer approval rule, or a conventional automation might solve the problem.
In its guide to building effective agents, Anthropic distinguishes predefined AI workflows from agents that dynamically choose their steps and tools. It recommends starting with the simplest solution that meets the need.
Our article Getting Started with AI Agents explores this architectural choice. For a business, the task should determine the technology.
Map what actually happens
The official process rarely captures every action needed to complete a task.
An invoice may officially move from receipt to validation to payment. In practice, someone renames the attachment, copies details into a spreadsheet, asks a colleague about a purchase order, and waits for approval through WhatsApp.
Those steps matter. They reveal the information gaps and informal decisions that keep the operation moving.
Map the workflow with the people who perform it. Follow several real cases from beginning to end, including ones that were delayed or required correction.
Record what starts the process, what information arrives, which systems are consulted, who makes each decision, and what marks completion. Pay attention to waiting time and repeated data entry.
The goal is a usable description of how work moves today. Without it, the agent will be designed around assumptions that employees already know are incomplete.
Make the data dependable
Connecting an agent to company data does not automatically give it a dependable picture of the business.
Consider a delivery workflow where the CRM contains the promised date, the warehouse system contains the dispatch status, and a spreadsheet records changes agreed with the customer. All three sources may be relevant, but they answer different questions.
Before automating, decide which source governs each fact. Define what should happen when sources conflict, when a record is missing, or when information has not been updated recently.
Shared identifiers also matter. If sales and operations refer to the same order using different numbers, the agent needs a reliable way to match them.
You do not need to consolidate every database before starting. You do need trustworthy information for the workflow you select, with clear rules for handling uncertainty.
Turn unwritten rules into explicit decisions
Employees often rely on experience to fill gaps in a process.
They know which customers need special handling, when a manager should review a request, and why a routine change could affect another department. That knowledge may never appear in a procedure.
An agent needs those decisions expressed clearly enough to follow and test.
Take a refund request. “Handle small refunds automatically” leaves several questions unanswered. What amount counts as small? Must the purchase be verified? Does the rule apply to every product? What happens after repeated requests?
Define the routine path and the conditions that require review. OpenAI’s practical guide to building agents recommends explicit actions and instructions for handling edge cases.
This exercise can expose disagreements between teams. Resolving them is part of preparing the workflow. A prompt should not quietly become the place where company policy is invented.
Give ownership and authority clear boundaries
A workflow needs someone responsible for its performance, including after automation is introduced.
That owner should be able to resolve process questions, coordinate changes, and review whether the system is producing results. Technical maintenance and business ownership may belong to different people, but both responsibilities must be assigned.
As we explore in From UX to AX: Autonomy without losing control, agents need defined authority. Reading an order, drafting a reply, changing a delivery date, and issuing a refund are separate permissions.
Start by deciding which actions it can perform independently, which need approval, and which remain outside its scope. Enforce those limits through the systems and tools it can access.
When human review is required, name the receiving role and establish a practical handoff. A case should arrive with the relevant records, the unresolved question, and the action awaiting approval.
Design for exceptions from the beginning
Routine cases make convincing demos. Exceptions reveal whether the workflow is ready for everyday use.
What happens when an integration is unavailable? When a customer supplies incomplete information? When an action succeeds but the confirmation fails?
Define how the system pauses, retries, requests clarification, or escalates. Where repeated actions could create duplicate records or transactions, add checks that prevent them.
Employees should be able to see what was attempted, what succeeded, and what still needs attention. Customers should receive an accurate status when the task cannot be completed.
A useful agent does not need to resolve every exception. It needs to recognize the limits of the workflow and leave the next person enough context to continue.
Pilot one workflow and measure the whole result
Choose a narrow workflow with a clear endpoint, accessible data, and manageable consequences if something goes wrong.
For delivery inquiries, the first version might retrieve verified information and draft a response for approval. That lets the team assess accuracy before allowing the system to communicate independently.
Measure current performance first. Track completion time, errors, manual effort, and the frequency of exceptions. During the pilot, include human review time and correction work alongside operating costs.
A faster draft provides little benefit if employees must spend longer verifying it. Evaluate the completed task and the effort required across the whole team.
Use those findings to improve the process, adjust permissions, and decide whether broader autonomy is justified.
Build on a process people can explain
Preparing a workflow does not require making the business perfect. It means making one selected process clear enough to support dependable automation.
That preparation creates value before the agent is deployed. Teams gain clearer responsibilities, better information, and fewer avoidable handoffs.
For businesses exploring AI with Zarego, workflow discovery provides a practical starting point: understand the operation, identify the useful intervention, and build around measurable outcomes.
The strongest foundation for an AI agent is a process whose goals, rules, and limits the business understands.
