What is agentic AI? Making sense of how AI agents work inside ERP

Agentic AI has become one of the most used (and least defined) buzzwords in enterprise software. Almost every vendor claims it among their list of features, usually to describe a basic chatbot with a new coat of paint.quence together on its own.

What makes AI ‘agentic’?

Agentic AI describes a system built to complete a task from start to finish, not just respond to what it's asked. It's given a goal, such as generate this quote, flag this exception or reschedule this order, then it works through however many steps that takes, inside clear rules and limitations set for it.

It acts, rather than just
answering questions

Ask a chatbot to draft a reply and it drafts a reply. It waits for the next instruction, then the next, but never does anything beyond what's been explicitly asked. An agent works differently. It can notice that something's happened, like if an email has landed or stock has dropped below a threshold. Then it gets on with the task that follows without waiting to be prompted at every step.

It works across several steps, not one exchange

Turning a raw inbound request into a finished quote means working through several steps: reading it, matching it to a product, checking stock, applying the right price and drafting the output. While a chatbot can help with pieces of that if it's fed the right information each time, an agent chains the whole sequence together on its own.

How do AI agents work inside an ERP?

An agent is only as useful as the data it can reach, which is where a natively ‘smart ERP’ has an advantage over a standalone AI tool bolted on from outside. An agent working inside the ERP already sits on top of production schedules, stock levels, pricing rules and customer history, rather than waiting for someone to feed it in.

That’s why best practice is to build your agentic AI capability into a core platform, instead of rebuilding it separately inside every product. When your AI agents sit at the platform level, the same underlying capability becomes available across all tools, systems and business intelligence dashboards that plug into them.

That platform-level design also decides what an agent can see and do for any given organisation. A fully cloud-hosted business gets the full range of agentic capability, since everything runs inside one connected environment. A business still fully or partly on-premise has more limited access, because some of that operational data sits outside the platform's reach.

What tasks can AI agents handle for an industrial business?

Here are four examples, by no means an exhaustive list:

1. Rescheduling around disruption

When a machine goes down or a tool breaks, the knock-on effect touches every order behind it. An agent can reschedule the affected orders in real time, working within the priority rules already in place, rather than waiting for someone to work through the list by hand.

2. The morning briefing

Production managers often arrive at the plant close to an hour before their shift starts, piecing together what happened overnight from whatever reports and messages came in. An agent can pull that picture together on its own: what ran, what didn't, what needs attention and then continue production accordingly. All before anyone walks through the door on a Monday morning.

3. Quote generation

A large share of inbound sales demand never gets acted on, not because it isn't wanted, but because it isn't worth the time. A £50 quote might take half an hour to put together once you’ve double checked stock, found the right product, worked out the price and responded to the customer. Modest jobs like this often get skipped in favour of bigger value work, but it all adds up.

An agent can take that same inbound request and turn it into a drafted quote within minutes, without anyone stopping what they're doing to process it.

4. Tariff code assignment

Assigning the correct tariff code to a product is a manual, rules-based task that eats time without needing much judgement. An agent can apply the same logic a person would, automatically, freeing that time for work that actually needs a person's attention.

How to tell a genuine AI agent from a rebadged chatbot

Practically every AI vendor now uses the word ‘agent.’ Make sure to ask the questions below if you want to cut through the label:

1. Does it finish the task, or hand you a draft?

An agent completes the work, or gets it to the point where a person only needs to approve it. If the output still needs someone to do the actual task afterwards, it's a drafting tool wearing agent branding.

2. Can it act across more than one step unprompted?

If a person has to type a new instruction at every stage, it's not agentic, however good the individual responses are.

3. Can an AI agent make decisions without human approval?

Within limits that are set deliberately, yes. Where that limit sits, and what always stays with a person, is a configuration choice, made for each workflow rather than fixed once for everything.

4. What sets it off: a person, or an event in the business?

An agent that only ever starts when someone types into a chat box is still fundamentally reactive. Genuine agentic capability responds to something happening (a stock level, an inbound email, a broken tool) without waiting for a prompt.

FAQs

Is an AI agent the same thing as agentic AI?

Agentic AI is the underlying approach. An AI agent is what that approach looks like in practice: a specific system built to carry out a defined task or set of tasks.

How is an AI agent different from RPA (robotic process automation)?

RPA follows a fixed, pre-programmed sequence of steps and breaks when anything changes. An AI agent reasons about the situation in front of it, the product, the price, the stock level, and adapts the sequence accordingly.

Does an AI agent need to be trained specifically on our data?

It needs access to your operational data, not a bespoke model trained from scratch. Inside an ERP, that data, stock, pricing, customer history, is already there for the agent to draw on.

Can an AI agent make decisions without human approval?

Within limits that are set deliberately, yes. Where that limit sits, and what always stays with a person, is a configuration choice, made for each workflow rather than fixed once for everything.

What's next:
From agents to prediction

Agentic AI carries out a task once the need for it is already clear, like the quote to write, the order to reschedule or the briefing to prepare. Predictive AI enables you to work one step earlier, surfacing that a margin issue or a stock problem exists before anyone has gone looking for it.

Our next guide picks up right where this one stops, moving from acting on a known problem to spotting one before it surfaces.

READ HERE

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