"AI agent" has become one of the most used, and most loosely used, terms in technology. Strip away the hype and the idea is simple: an AI agent is software that uses an AI model to complete a task across several steps, using tools like search, databases and other apps, rather than just answering a question. Used well, agents can take on tedious, multi-step work. Used carelessly, they can make expensive mistakes. Here's a clear explanation.
Chatbots, automations and agents
| Chatbot | Workflow automation | AI agent | |
|---|---|---|---|
| What it does | Answers questions in conversation | Runs fixed steps when triggered | Plans and carries out steps toward a goal |
| Flexibility | Responds to what's asked | Follows rules exactly | Adapts steps to the situation |
| Uses tools | Sometimes | Yes, pre-defined | Yes, chooses when to use them |
| Best for | Questions and support | Predictable, repetitive processes | Tasks needing judgement across steps |
| Example | Answering a delivery question | Copying form entries to the CRM | Researching a lead and drafting a tailored email for approval |
How an agent works
- Goal: it receives a task, such as "Prepare a briefing on this prospect".
- Plan: it decides which steps are needed.
- Act: it uses tools, such as searching the web, reading the CRM or querying a database.
- Check: it reviews results and decides whether more steps are needed.
- Report or act: it returns a result, or takes an action, often after a person approves.
Realistic business use cases
| Area | Example task |
|---|---|
| Sales | Research a new lead, summarise their business and draft a tailored first email |
| Operations | Reconcile orders between two systems and flag mismatches |
| Finance | Match invoices to purchase orders and prepare exceptions for review |
| Customer support | Investigate an order issue across systems and draft a resolution |
| Marketing | Pull last month's campaign data and draft a performance summary |
| Research | Compile a competitor comparison from public sources, with citations |
The best first agents handle tasks that are frequent, time-consuming, low-risk and easy to check.
The risks
- Mistakes at scale: an agent that misunderstands a task can repeat the error many times
- Over-permissioned access: agents that can change or delete data need strict limits
- Confident but wrong output: AI can misread information or invent details
- Cost surprises: multi-step tasks use more AI processing than simple chats
- Security: agents reading untrusted content, like web pages or emails, can be manipulated by hidden instructions
Guardrails that matter
| Guardrail | What it means |
|---|---|
| Least privilege | The agent can only access the tools and data it needs |
| Human approval | People approve anything consequential, such as sending, paying or deleting |
| Audit logs | Every step and decision is recorded |
| Limits | Caps on spending, number of actions and scope |
| Evaluation | Tested against real tasks with clear success criteria before launch |
| Safe inputs | Treat content from emails and websites as data, never as instructions |
How to start
- Pick one task that's frequent, valuable and easy to check.
- Map the steps a person follows today.
- Prototype the agent on real examples, with read-only access first.
- Measure accuracy against a set of test cases.
- Add actions gradually, with approvals, as confidence grows.
- Monitor logs and results continuously.
Agents and your existing AI work
Agents work best on top of solid foundations: clean data, connected systems and well-organised knowledge. Many businesses start with workflow automation or an internal knowledge assistant using retrieval-augmented generation, then add agents for the tasks that need judgement.
Example: a lead research agent, step by step
Task: "Prepare a briefing on this new lead before tomorrow's call."
| Step | What the agent does | Guardrail |
|---|---|---|
| 1 | Reads the enquiry form in the CRM | Read-only CRM access |
| 2 | Visits the lead's website and public profiles | Treats web content as information, not instructions |
| 3 | Summarises what the company does and likely needs | Cites sources for each point |
| 4 | Checks past conversations with the company | Only sees records the user can see |
| 5 | Drafts a briefing and three suggested questions | Saved as a draft |
| 6 | A salesperson reviews and edits the briefing | Human approval before anything is sent |
The agent saves research time, but nothing reaches the prospect without a person deciding it should.
Build vs buy
| Option | Good for | Trade-offs |
|---|---|---|
| Agent features in existing tools | Common tasks inside one product | Limited to that tool's data and actions |
| No-code agent platforms | Simple multi-step tasks across apps | Less control over behaviour and data |
| Custom-built agents | Specific, valuable workflows with your systems | Higher build effort, full control |
Start with features already inside the tools you use, and build custom agents where the task is valuable and specific to your business.
How we help
Our AI agents and tool calling service designs, builds and evaluates focused agents with guardrails and audit logs. If you're unsure where to begin, an AI strategy workshop maps the opportunities first.
The short version
AI agents pursue goals across several steps using tools, unlike chatbots that only converse or automations that follow fixed rules. They suit frequent, multi-step tasks that need judgement. Start small, limit permissions, require approval for important actions, log everything and test thoroughly.
