What Is an AI Agent? The Concept That Will Reshape How Your Business Operates

An AI assistant (like ChatGPT or Claude used in a standard conversation) is a reactive system: it produces an output in response to an input, and the cycle ends. The human decides what to do with the output.

An AI agent is a proactive system: it receives an objective, plans the steps required to achieve it, executes those steps (which may include using tools, searching the web, reading documents, writing and running code, or interacting with other systems), evaluates the results, adjusts its approach, and continues until the objective is met or it encounters a problem it cannot resolve.

The practical difference is significant. Ask an AI assistant to book a meeting and it will draft an email. Give an AI agent the same task and it might check your calendar, identify a mutual availability slot with the other party's calendar, draft and send the invitation, and send you a confirmation, all without you being involved in each step.

Agentic AI is not a future concept; it is in production in enterprise environments today, largely through Microsoft's Copilot ecosystem and Azure AI services.

Microsoft Copilot Agents can be configured to monitor incoming communications (emails, Teams messages, support tickets) and take defined actions in response: routing requests, retrieving relevant information, drafting responses, creating tasks, and escalating to human review when defined conditions are met.

Azure AI Agent Service provides the infrastructure for building more sophisticated AI agents that can interact with multiple business systems: creating records in CRM, retrieving data from databases, triggering workflows, and executing multi-step processes with appropriate human oversight points.

These deployments are typically scoped to specific business processes rather than given broad autonomous authority. An AI agent might be authorised to process routine expense claims autonomously but escalate all claims above a certain value or with unusual categories to a human reviewer.

AI agents create governance challenges that are qualitatively different from those of AI assistants. When an AI assistant makes an error, a human sees the output before any action is taken. When an AI agent makes an error, it may have already taken multiple actions in the business environment before a human notices.

Governance of AI agents requires answers to several questions: what actions is the agent authorised to take autonomously, and what requires human approval? How are the agent's actions logged and auditable? What are the escalation criteria that trigger human review? What is the recovery process if the agent takes an incorrect action? Who is accountable for the agent's actions?

The organisations deploying AI agents most responsibly are those that design the authorisation framework carefully before deployment: starting with narrow authority and expanding it as trust is established, rather than deploying broadly and constraining retrospectively when problems appear.

Boards should be asking their executive teams whether they have a governance framework for agentic AI that is distinct from their framework for AI assistants. The same governance that applies to an AI that produces text for human review does not adequately cover an AI that takes actions in business systems.

The risk appetite question is also different. How much autonomous AI action is the organisation comfortable with? In which domains? Under what oversight mechanisms? These are values and governance questions that require board-level engagement, not just technology decisions.