How to Build an AI Agent for Your Business

Agent or Workflow: Make the First Decision Honestly

The word "agent" gets applied to almost anything with a language model in it. A useful working definition: an agent is a model that decides its own next step, calls tools such as a CRM search or a calendar, looks at the result and repeats until the job is done. A workflow is a fixed sequence that you design, where the model handles one or two steps inside it, such as classifying an email or drafting a reply.

Anthropic's engineering guidance on building agents makes the same point practitioners learn the hard way: start with the simplest thing that works. Fixed workflows are cheaper, faster and far easier to test. Reach for a true agent loop only when the path genuinely varies, for example a support request that might need an order lookup, a warranty check, a shipping label or a handoff depending on what the customer says. If you are still unclear on the distinction, our explainer on what an AI agent is walks through examples.

Choosing the First Job

Good first agents share four traits. The task is frequent enough that automating it matters. It is bounded, with a clear done state. Mistakes are recoverable, so a wrong draft costs a few minutes rather than a lawsuit. And the information the agent needs already lives in systems it can reach.

Examples that tend to work for small and mid-sized businesses:

• Qualifying web form leads, asking two or three follow-up questions and booking a call.
• Answering "where is my order" or "when is my appointment" from live system data.
• Triaging a shared inbox into categories, drafting replies and routing the rest.
• Pulling data from invoices or intake forms into your accounting or practice software.

Poor first choices: anything that commits money, gives legal or medical advice, or depends on knowledge that exists only in one employee's head.

Tools, Data and Permissions

Every tool you give an agent is a function with a name, a description and a set of inputs. The model reads those descriptions to decide what to call, so vague descriptions produce wrong calls. Write them the way you would brief a new hire: what the tool does, when to use it, when not to, and what each field means.

Permissions deserve more care than prompts. OWASP's Top 10 for LLM Applications lists excessive agency as a core risk: an agent with broad write access can be steered into harmful actions by a confusing request or by text hidden in a document it reads. Practical rules:

• Use a dedicated service account for the agent, never an owner's login.
• Prefer narrow tools ("create appointment for this contact") over general ones ("run any API call").
• Validate tool inputs in code. The model proposes; your code checks that the date is in the future, the amount is within limits and the record belongs to this customer.
• Treat every email, web page and uploaded file the agent reads as untrusted. That content can carry instructions, which is how prompt injection works.

Standards such as the Model Context Protocol make it easier to plug the same tools into different models, but they do not decide what an agent should be allowed to do. That decision stays with you.

Testing Before Anyone Relies On It

A demo shows the agent handling the case you thought of. A test set shows how it handles the cases your customers actually send. Pull real past requests, strip personal data where possible, and write down the correct outcome for each. Include the awkward ones: angry customers, requests in Spanish if you serve South Florida, partial information, and someone trying to talk the agent into a discount.

Score each run on whether it reached the right outcome, followed policy, called the right tools and handed off when it should have. Rerun the whole set every time you change the instructions, the tools or the model, because a fix for one case often breaks another. Our guide to evaluating AI output quality covers scoring methods in more depth.

Running It in Production

Plan for the agent's first month to look like a supervised trainee. It drafts and proposes; a person approves. Log every conversation and tool call with timestamps so you can trace any complaint back to what the agent saw and did. Watch three numbers weekly: how often humans edit or reject its proposals, how often it escalates, and cost per completed task.

Common failure patterns to watch for: the agent loops on a tool that keeps returning errors, it answers confidently when the data it needed was missing, or it handles the happy path well but stalls on exceptions. Each one is fixed with clearer tool errors, an explicit "if you cannot find it, say so and hand off" rule, and more exception cases in the test set. If you would rather not run this yourself, DR3AM's AI automation team builds and maintains agents of this kind.