‘Agentic’ is doing a great deal of work in AI selling, usually without being defined. The distinction is simple. A chatbot answers a question. An agent is given an objective and works towards it in steps – choosing what to look up, using tools, drafting, checking its own output and deciding when it is done.

What an agent actually does

For example, an accountant or tax professional asks a question about a client letter about a termination payment.

In the case of Croner Intelligence the AI takes very clear steps to answer the query: interpret the question, retrieve the relevant authority, judge whether those sources settle the point, draft the advice, then produce it as a document.

A chatbot compresses all of that into a single act of writing. An agent runs it as a sequence, and can pause partway through to ask for a fact it does not have.

The cost of one agent that does everything

The obvious build is a single agent with instructions added until it handles every case. That works until it doesn’t. Instructions compete: the discipline required to cite an authority precisely pulls against the brevity wanted in a covering letter.

Improving one behaviour then degrades another, often invisibly – a change intended to tidy document layout can loosen sourcing rules three tasks away. Nor can anything be tested in isolation, because one set of instructions doing five jobs must be re-tested against all five whenever any of them changes.

Specialists with narrow briefs

The alternative is to divide the work into specialists with narrow briefs. Croner Intelligence uses separate sub-agents, each responsible for one part – research and retrieval, document generation, workflow steps.

Each has a short brief, defined inputs and outputs, and its own tests. Because they are separate components, an adjustment to how documents are formatted cannot reach into how sources are selected.

Specialising is not about making any single step cleverer; it keeps the effects of a change contained.

Why the architecture matters to an accountancy firm

The practical benefit is predictability. Behaviour holds steady across releases rather than drifting as the supplier tunes for something unrelated, and a poor answer can be traced to a component rather than to an opaque whole.

The parts carrying professional risk – basing answers on original, cited sources, naming missing facts – are governed by their own narrow instructions, insulated from work that has nothing to do with them.

One agent that does everything may seem a good idea on the surface, but it is superficial. The way Croner Intelligence divides the work with narrow agents gives reassurance the result is something a practice can rely on.

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