AI agents for companies: dependable, not fully autonomous

Individual AI tools each handle one step: summarise a text, read one field from a document, answer a question. An AI agent chains such steps into a system that takes on a task from start to finish. It plans the necessary steps, accesses your systems, makes decisions within a defined scope and comes back to a person when a judgement call is needed. That is the step from tools to systems.

The decisive word is scope. A good AI agent is not as autonomous as possible, it is deliberately bounded. Its task is clearly delimited, its actions are defined, and at fixed points it hands over to a human. Autonomy is not the goal, it is a measure you keep as small as the task allows, because everything the agent does on its own is something no one is watching.

The most important design decision in an AI agent is how the work is divided. A language model is strong at interpreting what is unclear: understanding a freely worded request, reading the right details out of an unstructured document, classifying a case. It is weak at calculating reliably, applying rules exactly and always returning the same result. So the agent takes on only the unclear, judgement-heavy parts. Everything that can be described precisely it hands to deterministic scripts and tools: a price calculation, a check against your master data, a call to your interface. It is exactly this split that makes the system verifiable.

A concrete example from our own work is an agent built around inbox.factoriq, factorIQ's document-extraction product for manufacturing. The agent monitors an inbox, recognises the purchase orders among the incoming email and routes them to inbox.factoriq for extraction. There the language model reads the structured details out of the PDF or scan: buyer and vendor, the addresses with their respective roles, and each line item with article, quantity, price and delivery date. The result then runs through a fixed set of checks that are plain code rather than model judgement: are all the required fields present, does the extracted total match the total printed on the document, does quantity times unit price add up to each line total, do the dates actually appear in the document. Every check has to pass for the order to move on automatically. A single failure sends it to a person for review and approval. Only then is the structured result handed over to the ERP system. The model does the interpreting, fixed tools do the routing and the integration, and a person decides at the one point where it counts.

An AI agent is not the right answer for every task. Where a process is not cleanly defined, where a single error cannot be tolerated, or where one tool already does the job, an agent is too much. We say this openly, because an agent in the wrong place costs the trust that an agent in the right place first has to earn.

Automation you cannot trust does not get used. So we build AI agents so that it stays clear at all times what they have done. Three things belong to that: a clearly delimited task, defined control points where a person takes over, and a full record of every decision the agent makes.

The biggest lever for trust is the split from the previous section. The more of the work that falls to fixed tools, the larger the part of the system that can be tested and checked for the same result every time. The model's judgement stays confined to the places where it is genuinely needed, and there a human is close enough to step in. That is what turns a demo into automation your team relies on.