What Is Agentic AI? A Plain-English Guide for Security Leaders

Agentic AI is a system that can carry out a task from start to finish rather than just answering a question about it and producing a defined outcome. It plans a sequence of steps, calls the tools or data sources it needs along the way, and acts, often without a person checking each individual step.

From Answering Questions to Doing the Work

The first wave of workplace AI was largely about assistance. Users asked a chatbot to summarise a document, draft an email, or search for an answer to a problem, and the same user read the AI’s output and decided how to proceed. That model created real governance questions around accuracy and acceptable use, but the human stayed in the loop for every action that mattered.

Agentic AI moves the human back a step. Now, a user can ask an AI agent to follow up on a sales opportunity and it might: retrieve recent customer emails, check the deal in a CRM system, draft a proposal from an approved template, and create a task for the account owner, all in one pass. Each of those processes is something a person used to do deliberately. Now, they happen because the agent decided they were the correct next steps.

Why Agentic AI Isn’t Just Generative AI with Extra Steps

Generative AI produces content. Agentic AI produces outcomes. That distinction sounds academic until you look at what each model requires behind the scenes to perform its function.

A generative AI tool needs a prompt and a model, whereas an AI agent needs an identity to act under, permissions to access the systems it touches, a set of tools or connectors it’s allowed to call, and a way to escalate when it hits a decision it shouldn’t make alone. In this way, agentic AI is closer to a human user than a standard productivity tool, and as such, should be treated the way any other user might be. That is, as an identity.

Traditional Applications Behaved Differently

A traditional business application follows a predictable path. It has known screens, defined APIs, a fixed set of permissions, and usually a named owner. Security teams built decades of practice around that shape: identity controls at the login, data loss prevention around the storage, audit logs around the actions.

An agent doesn’t respect those boundaries in the same way. It can interpret a loosely worded instruction, decide which of several data sources to search, combine information that was never meant to sit in the same document, and push a result into Teams, email, a ticketing system, or a line-of-business application, all based on its own judgement about what the task requires. A procurement agent that can legitimately read supplier records, review contracts, and send emails is entirely reasonable on paper. The same agent combining those three permissions in the wrong order could disclose confidential pricing to a supplier it should never have contacted, without any single step looking obviously wrong.

With Agentic AI, What Changes for Security Teams?

Three things tend to catch organisations out once agents move from pilot to production.

The first is identity. An agent needs an owner, a defined purpose, and a lifecycle, in the same way a contractor account does. Left unmanaged, agents accumulate the way old Teams groups and forgotten service accounts do.

The second is data. An agent is only as safe as the information it’s allowed to reach, and Microsoft 365 environments accumulate sharing permissions over years in ways nobody fully tracks. An agent doesn’t create that issue of gradual oversharing, it just finds it faster than a person would have.

The third is behaviour. Because agents act on instructions written in natural language, and because some of that language can be hidden within documents or web pages an agent retrieves, they open up an entirely new category of attack that traditional applications never had to worry about.

None of this means agents should be avoided. It means the access and autonomy given to an agent needs to be deliberate rather than assumed, in the same way you wouldn’t hand a new user unlimited permissions outside of their specific role and requirements.

FAQ

Is agentic AI the same as Microsoft Copilot?

Not exactly. Copilot started as an assistant that helps a person do their work. Microsoft has been extending it, and building separate tools like Copilot Studio, so that agents built on top of it can act with more autonomy. Whether a specific Copilot experience counts as “agentic” depends on how much it does without a person checking each step.

What's the practical difference between agentic AI and generative AI?

Generative AI produces a draft, an answer, or an image, and then stops its workflow. Agentic AI takes that output and uses it to decide what to do next, calling tools, retrieving further data, or triggering an action in another system.

Do agents need different security tools than the rest of Microsoft 365?

Not entirely different, but they do need to be brought into the tools you already have. Microsoft Entra, Purview and Defender all now extend their existing identity, data and threat protection capabilities to cover agents specifically, rather than requiring a separate stack.