What Are AI Agents and Should Your Business Use Them?
AI agents are AI systems that can plan and take actions across software, such as updating records or sending messages, rather than just answering questions. They can save real time on multi-step processes, but they need tight permissions, human approval points and monitoring because their mistakes become actions, not just words.
Written and reviewed by The Technology Office · Independent technology advisory
Key takeaways
- An AI agent goes beyond answering questions: it can plan steps and take actions in other systems on your behalf.
- Agents suit multi-step, rules-based processes with clear goals, such as triaging requests or preparing records for approval.
- Because agents act, their errors have consequences, so permissions, approval points, logging and the ability to stop them are essential.
- Start with agents that recommend or prepare work for a person to approve, and only extend autonomy once behaviour is proven.
The short answer
An AI agent is an AI system that can plan steps and take actions in other software, such as looking up records, updating systems or sending messages, rather than just answering a question. Agents can save real time on multi-step processes, but their mistakes become actions, not just words, so they need tight permissions, human approval points, logging and an off switch before they go anywhere near production.
What makes something an AI agent?
Most people first met generative AI as a chat window: you ask, it answers, you decide what to do. An agent adds three things:
- A goal – for example, process this supplier onboarding request.
- Tools – access to systems it can read from or act in, such as email, a CRM, a finance system or a ticketing platform.
- Autonomy – the ability to decide which step to take next, check results and continue until the goal is met or it needs help.
That combination is what makes agents powerful. It is also what makes them different from a chatbot in terms of risk.
Chatbots, automation and agents compared
| Chat assistant | Traditional automation | AI agent | |
|---|---|---|---|
| What it does | Answers and drafts | Follows fixed rules | Plans and takes actions toward a goal |
| Handles unstructured input | Yes | Poorly | Yes |
| Predictability | Output varies | Highly predictable | Behaviour varies |
| Acts in other systems | No, a person acts | Yes, as programmed | Yes, as it decides |
| Main risk | Wrong information | Breaks when inputs change | Wrong action taken at speed |
In practice, the best designs often mix all three: fixed automation for predictable steps, AI for interpreting messy inputs, and a person for judgement calls.
Where AI agents help businesses
Agents suit processes that have several steps, involve reading unstructured information and follow reasonably clear rules. Practical examples include:
- Request triage – reading incoming emails or forms, classifying them, gathering related records and routing them to the right person with a summary.
- Supplier or customer onboarding – checking that documents are complete, extracting details and preparing records for approval.
- IT service desk – resolving common requests such as access or password resets within strict limits, and escalating the rest.
- Research and preparation – compiling a briefing pack before a client meeting from internal systems.
- Finance exceptions – investigating unmatched transactions and proposing a resolution for a person to approve.
Notice the pattern. In most of these, the agent prepares and a person approves. That is where we recommend starting.
What are the risks of AI agents?
The risks of generative AI do not disappear with agents; they compound.
- Wrong actions – an agent that misreads a request can update the wrong record, send the wrong email or approve the wrong item, and do it repeatedly.
- Excessive access – agents are often given broad permissions for convenience, which turns them into an attractive target.
- Prompt injection – malicious instructions hidden in an email, document or web page can try to redirect what the agent does.
- Data exposure – agents that move information between systems can disclose personal or confidential data to the wrong place, with Privacy Act implications.
- Cost runaway – agents that loop or retry can generate unexpected usage charges.
- Accountability gaps – when nobody owns the agent, nobody notices when it drifts.
What controls do AI agents need?
Treat an agent like a new staff member with system access, not like a piece of software you install and forget. Before deployment, put these controls in place:
- Named business owner – a person accountable for what the agent does and for reviewing its performance.
- Least-privilege access – only the systems and actions needed for the task, with read-only access wherever possible.
- Human approval points – any action that moves money, changes customer records, sends external communications or deletes data needs approval until proven otherwise.
- Limits – caps on volume, value and spend per day.
- Full logging – every input, decision and action recorded so you can investigate and audit.
- Kill switch – a simple, tested way to pause the agent immediately.
- Testing with hostile inputs – deliberately try to mislead the agent before real users do.
These controls fit naturally into an AI governance framework. Our guide on building an AI governance framework explains how to set one up, and frameworks such as the NIST AI Risk Management Framework offer a useful reference structure. Security leaders should also bring agents within the scope of cybersecurity governance.
A sensible path to adopting agents
| Stage | Agent role | Human role |
|---|---|---|
| 1. Assist | Gathers information and drafts | Does the work, using the draft |
| 2. Prepare | Prepares complete actions for approval | Approves or rejects each action |
| 3. Act within limits | Acts on low-risk, high-confidence cases | Reviews samples and exceptions |
| 4. Broader autonomy | Acts across more cases | Monitors performance and audits |
Move up a stage only when the evidence from the previous stage supports it. Many businesses will find stage 2 delivers most of the value with a fraction of the risk.
Should you build or buy agents?
Agent capabilities are now appearing inside mainstream platforms such as productivity suites, CRMs and service desk tools, alongside developer frameworks for building your own. Buying embedded agents is usually faster and easier to govern; building makes sense when the process is distinctive and valuable. Our article on build vs buy AI covers the trade-offs, and an AI readiness assessment will show whether your data and processes are ready for either.
How The Technology Office helps businesses use AI agents safely
The Technology Office helps leadership teams separate genuine agent opportunities from vendor hype. As an independent advisory that sells no software, we assess which processes suit agents, design the permissions, approval points and monitoring, and run measured pilots against a baseline so you can see the time saved and the error rate before extending autonomy.
Our AI consulting and AI governance consulting services bring a CIO-level perspective to these decisions, balancing productivity, security and accountability. We work with businesses of every size and industry across Australia.
Frequently asked questions
What is an AI agent in simple terms?
An AI agent is software that uses an AI model to work towards a goal by deciding on steps and using tools, such as searching systems, filling in forms, updating records or sending messages. A chatbot answers; an agent acts.
How is an AI agent different from automation?
Traditional automation follows fixed rules written in advance. An AI agent can interpret unstructured information and choose its next step, which makes it more flexible but also less predictable, so it needs stronger controls.
Are AI agents safe for business use?
They can be, with the right controls: least-privilege access, human approval for consequential actions, complete activity logs, spending and volume limits, and a way to switch the agent off. Without those controls, they introduce real operational and security risk.
What is a good first AI agent for a business?
One that prepares work for a person to approve, such as reading incoming requests, gathering the relevant information and drafting a response or record. This captures much of the time saving while keeping a human in control.
Who is accountable when an AI agent makes a mistake?
The business deploying the agent remains accountable for its actions, just as it is for staff and other systems. That is why each agent needs a named business owner and clear limits on what it can do.
Sources and further reading
- AI Risk Management Framework — National Institute of Standards and Technology (NIST)
- Guidance on privacy and the use of commercially available AI products — Office of the Australian Information Commissioner (OAIC)
Mentioned services
These service and regional NSW pages expand on the topics covered in this article.
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