AI and automation
Agentic AI
Agentic artificial intelligence
Agentic AI describes AI systems built to pursue a goal across multiple steps with a meaningful degree of autonomy. Such a system plans a sequence of actions, calls tools or APIs, evaluates the results, and adjusts its next step accordingly, rather than following the single-turn, prompt-in-response-out pattern of a conventional chatbot or Copilot conversation.
Why Agentic AI matters in a Microsoft estate
Agentic AI matters when teams want AI support without losing control of identity, data, approvals, and audit evidence. In the EfficientEther portfolio, AI terms usually connect to governed workflows, Copilot readiness, or agentic packaging where humans still review important outputs.
How Agentic AI shows up in practice
The distinguishing characteristic is not the underlying language model, typically the same class of large language model powering conversational AI, but the surrounding architecture. An agent is given a defined objective, access to a set of tools or connectors it can invoke, searching a mailbox, querying a database, updating a record in a line-of-business system, calling another agent, and some form of loop that lets it take an action, observe the outcome, and decide whether the goal has been met or another step is needed. All of this happens without a human approving each individual action along the way.
In the Microsoft ecosystem this shows up concretely through Copilot Studio, which lets organisations build custom agents that can be assigned specific tools and data sources and deployed into Teams or as standalone assistants. It also shows up through the autonomous and multi-agent capabilities being layered onto Microsoft 365 Copilot itself, for scenarios like automatically triaging and drafting responses to routine requests. A third route is Azure AI Foundry's agent service, for organisations building custom, code-first agentic applications rather than using a low-code builder. The genuinely new risk surface agentic AI introduces, compared to conversational AI, is that autonomy directly multiplies the consequence of any given mistake.
A conversational Copilot that hallucinates a wrong answer produces a wrong answer a human then reads and can catch, whereas an agent with write access to a system that acts on a flawed plan or a hallucinated intermediate result can take a real, consequential action, sending an email, modifying a record, approving a request, before any human reviews what it did. This is why permission scoping for agents needs to be treated with at least the rigor of scoping a service account or an automation script, granting only the specific tools and data access the agent's task genuinely requires rather than the broad access convenient for whoever built it.
Governance for agentic AI in a Microsoft 365 tenant is still an evolving area operationally. Agent identities, what they can access, what actions they've taken, and how that activity is logged and audited, need to be tracked with the same seriousness as human account permissions. Organisations adopting Copilot Studio agents at scale without a clear inventory of what agents exist, what data sources and tools each one has been granted, and who owns and reviews them are building the same kind of unmanaged sprawl that ungoverned Power Automate flows and Power Apps created in the low-code era before it, just with materially higher-stakes autonomous actions attached.