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What Is a Custom AI Agent? A Plain-English Guide for Business Owners
AI agents are more than chatbots. What they are, how they differ from simple automation, where they help and how to keep them under control.
"AI agent" is a term used loosely. In plain terms, an agent is a piece of software that is given a goal, decides which steps to take, uses tools such as your email, CRM or calendar to take them, and checks its own progress. It is different from a chatbot that only answers, and from a fixed automation that follows a script.
Automation, chatbot, agent: what is the difference?
| Fixed automation | Chatbot or assistant | AI agent |
|---|
| What it does | Runs the same steps every time | Answers questions in conversation | Works towards a goal and chooses steps |
| Handles variety | Poorly | Moderately | Well, within its limits |
| Acts in other tools | Yes, as scripted | Sometimes | Yes, from a permitted set of tools |
| Predictability | Very high | Medium | Lower, so it needs guardrails |
| Best for | Repeated, rule-based processes | Support and information | Messy, multi-step tasks |
Think of fixed automation as a conveyor belt, a chatbot as a receptionist, and an agent as a capable assistant who can take a request, work out what is needed and get it done, within the limits you set.
Examples of tasks where an agent can help
- Inbox triage. Reading incoming emails, working out the type of request, looking up the customer, drafting a reply and creating a task.
- Quote preparation. Gathering details from an enquiry, checking a price list, filling a template and sending it for approval.
- Research and summaries. Collecting information from several internal sources and summarising it for a manager.
- Data clean-up. Finding duplicates, standardising fields and flagging uncertain matches.
- Scheduling. Coordinating availability across people and sending confirmations.
In each case a person still approves the outcome that matters, such as sending a quote or changing a record.
What keeps an agent safe
- A narrow job. One clear responsibility, not "handle everything".
- Least privilege. Give access only to the tools and data the job needs, with read-only access where that is enough.
- Approval steps. Require a human to approve actions that spend money, send external messages or change important records.
- Limits. Cap the number of steps, the amount it may spend and the time it may run.
- Logging. Record what it saw, what it decided and what it did, so any action can be reviewed.
- Testing. Try it on real examples, including difficult ones, before it touches live systems.
- A way to stop. A simple switch to pause it, and a fallback to a person.
The test of a good agent project. Can you describe the job in two sentences, name the tools it may use, and say what a person approves? If not, the scope is still too loose.
When not to use an agent
Agents add flexibility, and with it complexity and risk. If the steps never vary, a fixed workflow is simpler, cheaper and easier to trust. If the cost of a mistake is high and can't be checked by a person, don't hand it to an agent. Use agents where the inputs are messy, the steps vary and a person can review the result.
From idea to working agent
- Write the task as a short procedure you would give a capable new colleague.
- List the tools and data it needs, and what it must never touch.
- Decide the approval points and the stop conditions.
- Build a small version and test it on real examples.
- Run it alongside your team before it takes live actions.
- Review the logs weekly and widen the scope slowly.
A worked example (illustrative)
Imagine a small distributor that receives quote requests by email. The steps below are a made-up example of how an agent and a person could share the work.
| Step | The agent | The person |
|---|
| 1. Read the email | Pulls out the products, quantities and delivery location. | Nothing yet. |
| 2. Check details | Looks up the customer, the price list and stock in the permitted systems. | Nothing yet. |
| 3. Draft the quote | Fills the quote template and notes anything it is unsure about. | Reviews the draft and the notes. |
| 4. Approve and send | Waits for approval. | Approves, edits or rejects, then sends. |
| 5. Record | Logs what it read, what it checked and what was sent. | Reviews the log when needed. |
The agent does the reading, looking up and drafting. The person keeps the judgement and the final say. That split is typical of agents that earn trust.
Building one with us
Webbit builds custom AI agents and integrations that connect the tools you already use, with permissions, approval steps and logging designed in. Read our technical capabilities page for the detail on access, reliability and limits, or start with an automation audit to decide whether an agent is the right tool. For a checklist you can use with any provider, see AI automation without the risk.
Frequently asked questions
Is an AI agent the same as ChatGPT?
No. A chat assistant answers in conversation. An agent is built around a goal and a set of tools, and can take actions in your systems within defined limits. It may use a language model, but the design around it matters as much as the model.
Will an agent replace my team?
In practice agents take over repetitive steps, such as gathering information and drafting, so people can focus on decisions and customers. People remain responsible for approvals and exceptions.
How do we know what it did?
Through logs. A well-built agent records its inputs, decisions and actions so you can review any result and improve the design.
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