AI agents aren’t magic – and believing they are could break your business

By Matt Hyde, CTO of CloudWize

Advancements in artificial intelligence have really excited progressive business leaders. And it’s hardly surprising. From a technological perspective, we’ve never had a bigger opportunity to turn ideation into action. So, consequently, we see many organisations embarking on major AI journeys, inspired by the ‘art of the possible’.

For someone who has spent their whole career in the world of digital transformation, this appetite is electrifying – especially because, to some extent, AI has levelled the playing field for smaller companies. Transformation on the scale we’re now seeing was once reserved only for vast enterprises, or those divesting their budgets to lower cost – and often lower quality – offshore engineering.

But despite this wealth of opportunity, comes risk. 

MIT data suggests that 95% of AI projects fail to generate an ROI, leaving critics quick to express cynicism about the difference AI can actually make. Sadly, the technology itself often takes the ‘blame’. But there are many reasons projects ‘fail’, including organisations jumping straight to ‘hero’ use cases before they address the micro-processes that would really move the needle; a lack of success measurement clarity; weak transformative leadership; cultural adoption oversight; data flaws and poor governance  – to name just a few. 

It’s therefore important to push beyond the bold headlines. Because if you read more than the hype, you can keep moving towards your ‘North Star’ without becoming one of the statistics.

Agentic AI — advancing digital maturity

For businesses a little further on the AI maturity curve – who are capable of transformation beyond simple co-pilots, chatbots and process automation – we have agentic AI. 

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 required, this AI ‘brain’ can operate, reason and learn, without human intervention. 

In a relatively straightforward example, a legal briefing agent could receive an input query, retrieve information and apply deep learning in order to summarise meaningful findings in a case report. Others keen to really leverage its potential might design a multi-agent orchestrator to manage a customer’s e-commerce order end-to-end, including fulfilment.

Whatever the use case, AI agents can certainly do much more than follow a script. They are intelligent enough to follow intricate, multi-step activities, think independently, and handle variation.

Are AI agents really autonomous digital beings?

But this doesn’t mean they are fully autonomous digital beings – if they were, you wouldn’t want them anywhere near your business. 

They work within the boundaries you define. They never decide what is allowed. 

So, instead of viewing them as ‘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 company 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. 

The importance of guardrails

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.

If your AI can’t explain its guardrails, it isn’t production ready.

Human feedback loops are 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 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 well-defined, documented and deeply understood. This might be a mundane, repetitious ‘back office’ activity such as timesheets and billing. Of course this is important, but it represents high volume ‘admin’ that stifles colleague productivity. Agents can augment this human workload, to give colleagues time back to complete the value-oriented parts of the role requiring deeper thought. 

HR onboarding is another fantastic use case. I’ve seen HR agents help handle up to 60% more job applications while maintaining a fair and compliant process, therefore accelerating an effective recruitment strategy while ensuring wider people management initiatives are maintained during busy periods. 

In truth, agentic AI can thrive anywhere a level-4 process or Service Operational Procedure (SOP) can be defined – even 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.

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