Generative AI helped organisations ask better questions of information. Agentic AI raises a harder possibility: software that can plan and act. The opportunity is significant, but so is the need to define exactly what an agent may do, what evidence it must retain and when a person must remain in control.

Start with a job, not an agent

The strongest use cases are bounded pieces of work with a clear objective, known tools and an observable result. Case preparation, evidence collation, service triage and routine operational coordination are often better candidates than open-ended decision making.

A useful discovery exercise maps the current workflow, exceptions, authority levels and cost of failure. This prevents a technically impressive agent from being applied to a process that is unstable, poorly understood or unsuitable for autonomy.

  • Is the objective unambiguous?
  • Can success and failure be observed?
  • Are the tools and data sources governed?
  • Is there a clear escalation path?

Autonomy is a design variable

Agentic systems do not need to be either fully manual or fully autonomous. They can draft a plan, recommend an action, complete low-risk steps and request approval before a material commitment. The appropriate level depends on impact, reversibility, confidence and the sensitivity of the data involved.

Permissions should be least-privilege and task-specific. A customer-service agent may be allowed to read an order and draft a response, while a refund or contractual change remains subject to explicit approval.

Evaluation becomes part of the product

Traditional software tests whether defined inputs produce defined outputs. Agents introduce variability in planning, tool selection and language. Evaluation therefore needs representative task suites, adversarial cases, policy checks and measures for completion quality, cost, latency and escalation.

Production traces should record the instructions, evidence, model, tools, actions and human interventions required to reconstruct what happened. That operational evidence supports improvement and accountability.

  • Task completion and factual grounding
  • Policy and permission compliance
  • Safe failure and escalation
  • Cost, latency and user effort

A controlled route to value

Begin with a narrow workflow, limit the accessible tools and run the agent alongside the current process. Compare outcomes, collect failure modes and expand authority only when the evidence supports it.

Agentic AI is most valuable when it removes coordination friction while making work more visible. The goal is not autonomy for its own sake; it is dependable action with a clear line of accountability.

Turn the thinking into action.

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