Digital colleagues that prepare, identify and execute.

An AI agent can gather information, interpret defined inputs, use approved tools and prepare or execute repeatable tasks within set boundaries. Useful applications include analysis, reporting, content preparation and workflow support. The right task depends on available data, risk and measurable value; an agent should solve a specific operational problem rather than exist as a demonstration.

Standard automation usually follows fixed rules, while an AI agent can interpret context and choose among permitted steps. That flexibility also creates additional control requirements. We define the tools, sources, approval points and exception handling in advance, so the agent has enough freedom to be useful without receiving unrestricted access to systems or decisions.

Only when the task, risk and agreed controls make that appropriate. Publication, budget changes, sensitive claims and exceptions can remain subject to human approval. Lower-risk preparation or monitoring may run automatically. The autonomy level is therefore a design choice for each workflow, not a general promise that every agent operates independently.

Yes, when the relevant sources can be connected under suitable access and processing conditions. The agent does not need access to every system. We identify the minimum information required, separate sensitive steps where needed and record which sources and actions are permitted. Data quality and ownership remain important because an agent cannot correct every source error automatically.

We assess repetition, information quality, business value, risk, available tools and the ability to verify output. A suitable task has a clear objective and observable result. Highly ambiguous decisions, sparse data or consequences that cannot be reviewed may call for human work or conventional software instead of an AI agent.