Agentic AI

What is Agentic AI?

Much of today’s agentic AI places a generative model inside a goal-directed loop and gives the system authority to plan, use tools, evaluate progress, and decide what to do next. Used as a conventional assistant, a generative model produces an output and waits for a person to decide whether it is good enough, what to fix, and what should happen next. An agentic system is authorised to make more of those intermediate choices itself: it plans against a goal, invokes tools and APIs under scoped permissions, critiques the output, and iterates until the work meets the condition it was set. The organisation still defines the objective and the boundaries; the human no longer directs every step. The models underneath are familiar. What changes is the architecture around them and the authority it is given.

The pieces assembled quickly. Agentic systems can now operate real software directly, an open standard has emerged for connecting them to enterprise tools and data, and major platforms have moved from research demonstrations into commercial products. Gartner predictsgartner.comGartner: Over 40% of Agentic AI Projects Will Be Canceled by End 2027The prediction that over 40% of agentic AI projects will be cancelled by end-2027, largely through impatience with early returns.Published 25 June 2025Open link Archived copy that more than 40% of agentic AI projects will be cancelled by the end of 2027 because of escalating costs, unclear business value, or inadequate risk controls. That divergence between commercial momentum and project attrition is the signal. Organisations can spend heavily on agentic capability and still destroy value if they delegate judgement without deciding where that delegation belongs.

Why it matters to Boards

An agentic deployment is not merely a tool purchase; it is a delegation of decision authority. When a human drives the loop, they make countless micro-decisions: whether the work is complete, whether it answers the question, which source to check next, and when to stop. When the system drives the loop, it exercises more of those judgements autonomously, at a scale and speed no management reporting line can observe action by action. The Board cannot inspect every intermediate action, but it remains accountable for the governance system that determines what the agent may do, when it must escalate, how its actions are recorded, and who owns the consequences. This is the shift I have been writing about since The Board in the Machine: organisations moving from overseeing decisions made by people to governing decisions increasingly shaped or executed by systems, while accountability remains with the humans who delegated the authority.

The second reason for Board attention is attrition risk. Gartner attributes its forecast of widespread cancellations to escalating costs, unclear business value, and inadequate risk controls. Agentic pilots also inherit familiar problems from earlier AI programmes, including weak workflow integration and poor measurement, then add failure modes of their own because autonomous operation can take actions a copilot cannot. The organisations positioned to avoid that attrition will treat agentic AI as bounded delegation: failure must be recoverable, success criteria must exist before the spend, and escalation and audit mechanisms must be tested as part of the pilot. Management should also establish whether autonomous loops are already operating informally or outside approved controls. Their existence should be discovered through an inventory, not assumed either way.

Questions Boards are asking

References