Advancements in artificial intelligence have really excited progressive business leaders. It’s unsurprising. From a technological perspective, we’ve never had a bigger opportunity to turn ideation into action. That’s why many organisations are embarking on major AI journeys, inspired by the ‘art of the possible’.

But despite this wealth of opportunity, comes risk, because hype can lead to misunderstandings.

Are AI agents really autonomous digital beings?

Take AI agents.

Unlike a prompt-driven or conversational Large Language Model (LLM), agentic AI is goal-orientated. With access to knowledge, a set of defined instructions outlining what it’s trying to achieve, and the ability to connect to other tools and systems to collect the data it needs, this AI ‘brain’ can operate, reason and learn, without human intervention.

Let’s apply that to a real world use case such as accounts payable. Whereas traditional automation would struggle with inconsistent data across different invoice layouts, supplier-specific quirks or missing fields, a fairly straightforward finance agent could handle invoicing, expenses and reconciliations with ease, alleviating admin by 47%. I’ve also seen an incident outage agent reduce IT downtime by 30%, in what was again a simple use case. But of course the possibilities really are endless, with multi-agent orchestrations capable of handling complex end-to-end e-commerce orders, inclusive of customer fulfilment, for example.

Whatever the brief, AI agents can do much more than follow a script. They’re intelligent enough to follow intricate, multi-step activities, think independently, and handle variation. But this doesn’t mean they’re fully autonomous digital beings – if they were, you wouldn’t want them anywhere near your business.

The importance of guardrails

AI agents work within the boundaries you define. They never decide what’s allowed.

So, instead of considering them ‘magicians’, think of them as digital workers with a job description. Like any employee, they should have a clear scope of responsibility, follow established processes, use approved systems and tools, and adhere to policies. Importantly, they need to know to stop when something doesn’t look right, and escalate instead of guessing. Because they may be able to handle ambiguity but you don’t want them to bypass governance.

If your AI can’t explain its guardrails, it isn’t production ready. Guardrails make AI safe, useful, and deployable in real business environments. Without these ‘rules’, agents will ‘hallucinate’ or attempt to fill the gaps of what they don’t know. You therefore need to clearly define outcomes, policies, and accuracy thresholds, early on. Otherwise, the risk of non-compliance and data leakage escalates exponentially.

Human feedback loops remain important too, to validate outputs and train the AI on how to behave, especially when confidence is low. This also provides traceability and auditability, so that if anything goes wrong, you can quickly track why, to enable improvement.

How to deploy AI agents safely

To ensure safe progress, that will not break a business, start by identifying processes that are already well-defined, documented and deeply understood. This might be a mundane, repetitious ‘back office’ activity such as timesheets and billing, or a complex, time consuming change management workflow, reliant on multiple interconnected people, systems and steps.

If agents can safely augment this human workload, freeing up colleagues to complete the value-oriented parts of the role requiring deeper thought, you will improve productivity and accelerate change.

Truthfully, agentic AI can thrive anywhere a level-4 process or Service Operational Procedure (SOP) can be defined – even in highly-regulated or compliance-driven environments. But AI agents aren’t magic, and that’s the point.

The intelligence helps with the messy parts. The rules decide what’s allowed.