What an agent is
An AI agent combines model reasoning with tools, instructions, context, and permissions so it can help complete a workflow. The difference from a chat assistant is the loop: an agent can take an action, look at the result, and decide what to do next, such as searching a knowledge base, drafting a reply, or updating a record, all within the boundaries it has been given. Both OpenAI and Anthropic publish developer documentation describing how these tool-using systems work, which is useful background even if nobody on the team writes code.
Source notes: OpenAI platform documentation, Anthropic Claude documentation
What agents need
Good agents need boundaries: the job, the tools, the data they may access, logs, and a fallback when confidence is low. Treat it like onboarding a junior staff member. A new hire does not get every system password and zero supervision on day one. An agent needs the same staged trust: a narrow job description, access to only the systems the job requires, a record of everything it did, and a clear rule for when it must stop and hand the task to a person.
Where agents fit
Agents are useful for research, drafting, triage, reporting, document workflows, content operations, and controlled support work. The common thread is work that is structured, repeatable, and reviewable. If a competent contractor could do the task from a written brief and a checklist, an agent is worth evaluating. If the task needs judgement you would only trust a senior person with, keep the agent in a support role, preparing options rather than making the call. Start with internal-facing work, where a mistake costs an edit rather than a customer.
Why design systems matter
Open Design-style agent systems keep prompts, components, examples, governance, and handover notes in one place so the team can reuse the pattern. Without that documentation, every agent is a one-off experiment living in someone's chat history. With it, the second and third agents cost a fraction of the first, because the team starts from proven instructions and a list of known failure modes. Documenting failures matters as much as documenting wins, because the failure list is what stops the next person repeating them.
What not to automate
Do not hand an agent legal, financial, HR, safety, security, or customer-impacting authority without proper review and accountability. Decisions involving personal information also carry privacy obligations, and the OAIC's guidance for Australian organisations is the place to check before an agent touches customer records. The test is simple: if a mistake would need an apology, a refund, or a lawyer, the agent prepares and a person decides.
Source notes: OAIC privacy guidance
A worked example: an enquiry triage agent
A trades business receives around thirty website enquiries a week. The agent's job is narrow: read each enquiry, classify it as a quote request, warranty claim, supplier message, or spam, draft a tailored reply, and queue it for the office manager. The agent can read the enquiry form and the service price list. It cannot send email, see invoices, or change records. Every draft sits in a review queue, and anything the agent cannot classify confidently goes to a person untouched. The office manager went from writing thirty replies a week to approving thirty drafts, roughly four hours saved, with no customer-facing action ever taken by the agent alone.
Common mistakes with first agents
Giving the agent a job too broad to measure. Connecting tools it might need someday instead of scoping access to the actual task. Skipping logs, which turns every failure into a mystery. Letting the agent act on customers directly before the review queue has proven the drafts are reliable. And measuring novelty instead of outcomes: an agent that quietly saves four hours a week beats an impressive demo nobody trusts.