"AI agents" are everywhere in technology news, but what can they actually do for a business today — and what should you avoid? This guide explains AI agents for business in practical terms: what they are, where they work, how to design them and how to keep them safe.
From chatbots to agents
- A chatbot answers questions in a conversation.
- An assistant with RAG answers from your own content — see what is RAG.
- An agent pursues a goal over several steps: it decides which tools to call (search, APIs, calculations), looks at the results and decides the next step until the task is done or it needs a human.
Practical use cases
- Support triage: read a ticket, look up the customer and order, classify the issue, draft a reply and route it — with an agent handing off anything uncertain.
- Back-office automation: extract data from invoices or forms, check it against the ERP, and prepare entries for approval.
- Sales operations: research an account from CRM notes and public information, and prepare a meeting brief.
- Internal IT and HR requests: answer policy questions and perform simple, well-defined actions through existing systems.
- Engineering: summarize incidents from logs and tickets, or draft release notes from merged pull requests.
The best early use cases have clear success criteria, existing APIs and a low cost when something goes wrong.
A reference architecture
- Orchestrator in your backend (for example ASP.NET Core or Node.js) that runs the agent loop, enforces limits and stores state.
- Model accessed through an LLM API with tool/function calling.
- Tools — narrow, well-documented functions that wrap your existing APIs:
GetOrder,SearchKnowledgeBase,CreateDraftReply. Each tool checks permissions itself. - Approval step for actions that cost money, contact customers or change records.
- Audit log of every prompt, tool call, result and decision.
- Evaluation suite of realistic tasks with expected outcomes, run before every change.
Design principles
- Least privilege: the agent acts with the permissions of the user or a restricted service account — never an admin key.
- Small, typed tools: prefer
RefundOrder(orderId, amount)with validation over a generic "run SQL" tool. - Humans in the loop for irreversible or high-value actions.
- Step and cost limits: cap the number of iterations, tokens and time per task.
- Deterministic where possible: use normal code for rules and calculations; use the model for judgment and language.
Risks to manage
| Risk | Mitigation |
|---|---|
| Prompt injection from documents, emails or web pages | Treat retrieved content as data, restrict tools, require approval for sensitive actions |
| Wrong or invented actions | Validate tool arguments, confirm with the user, evaluate on real tasks |
| Data leakage | Minimize data sent to the model, filter by permissions, review provider terms |
| Runaway cost or loops | Iteration, token and time limits; monitoring and alerts |
| No accountability | Full audit trail and clear ownership of each agent |
The OWASP Top 10 for LLM Applications is a good security checklist for agent projects.
How to start
- Pick one workflow with measurable value and low risk.
- Collect 30–50 real examples with the correct outcome.
- Build a narrow agent with two or three tools and a mandatory approval step.
- Run it in "suggest" mode alongside people, compare results, then gradually automate the steps that prove reliable.
Key takeaways: agents plan and act through tools. Start with narrow, low-risk workflows, give agents minimal permissions, keep humans approving important actions, and evaluate and log everything.
Exploring agents for your operations? Our AI application development team builds agents on top of your existing APIs, and our AI integration services cover the surrounding security and evaluation work.
