Published: May 12, 2026 at 1:03 pm
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.