Enterprise AI
Microsoft Agent 365
Also known as: Agent 365, Microsoft Agent365
Microsoft Agent 365 is a Microsoft control plane for managing AI agents across an organisation, announced at Microsoft Ignite in November 2025. It is designed to give IT teams a way to register, govern, secure and monitor agents in a similar way to how they manage user accounts. The aim is to bring agents built in Copilot Studio, Foundry and third party tools under one set of identity, security and compliance controls.
What it is
Agent 365 is positioned as an administrative layer rather than an end user chat product. It brings together an inventory of the agents operating in a tenant, identity for those agents, access controls, and visibility into what they are doing. Microsoft has described it as working with its wider stack, including agent identity in Entra, security tooling in Defender and data governance in Purview.
Why it matters
As organisations deploy more agents, the governance question shifts from which model to use to who owns each agent, what data it can reach and what it did last week. For marketing and growth teams, that matters because content, campaign and analytics agents will only be approved at scale if they fit an enterprise control framework. Teams that understand the control plane get their agents into production faster and with fewer security objections.
How it works
Administrators register agents so each one has an identity and an accountable human owner, then assign permissions that scope the data and systems the agent can touch. Activity is logged so that agent actions can be audited, investigated and reported on alongside human activity. In practice, adoption tends to start with a small set of approved agents, a clear onboarding process and regular reviews of which agents are still in use.
When it applies
It applies in Microsoft 365 organisations that are moving beyond pilot agents into wider deployment, and in any procurement or security review where the question is how agents will be governed.
Examples
- An IT team maintains a register of every agent running in the tenant, each with a named owner, a stated business purpose and a review date.
- A marketing operations lead submits a campaign reporting agent for approval and is asked to document exactly which data sources it needs before permissions are granted.
- A security analyst reviews agent activity logs after an unexpected spike in document access and traces it to a misconfigured agent rather than a user account.
How it is measured
- Number of registered agents in the tenant, split by owner and business function
- Share of active agents with a named owner, documented purpose and review date
- Count of agent related security or data access incidents per quarter
- Time from agent build request to approved production deployment
Insights on Microsoft Agent 365
Related terms in Enterprise AI
- AI agentsAI agents are software systems that use a language model to plan and carry out multi-step tasks, rather than simply returning a block of text. They can call tools, query APIs, browse websites and write to other systems in pursuit of a goal, with varying degrees of human oversight. The term covers everything from a scripted assistant that books a meeting to a research agent that gathers sources and drafts a report.
- AI chipsAI chips are processors designed or optimised to run machine learning workloads, especially the large matrix operations behind training and inference. The category covers GPUs, tensor and neural processing units, and custom ASICs, usually paired with high-bandwidth memory and fast interconnects. They are also called AI accelerators or AI semiconductors.
- AI cost managementAI cost management is the practice of tracking, forecasting and controlling what an organisation spends on AI systems, including model API calls, inference compute, vector storage and the tooling around them. It applies familiar financial discipline to usage that is variable, token based and easy to scale without noticing. The goal is predictable spend at an acceptable level of quality and latency, rather than the lowest possible bill.
- AI drug discoveryAI drug discovery is the use of machine learning and computational models to support the early stages of finding and refining new medicines. Models are applied to tasks such as predicting protein structures, identifying candidate molecules, prioritising targets and estimating properties like toxicity or binding affinity. The aim is to narrow a very large search space before expensive laboratory and clinical work begins.
- AI infrastructureAI infrastructure is the stack of hardware, networking, storage and software needed to train, fine-tune and serve AI models at scale. It spans accelerators such as GPUs, the data centres and power that house them, and the orchestration and serving layers that turn raw compute into working model endpoints. For most marketing teams it is a cost and capacity constraint they consume through APIs rather than something they build.
- AI procurementAI procurement is the process of evaluating, buying and contracting artificial intelligence tools and services, from assistants and content platforms to models accessed through an application programming interface. It covers commercial terms alongside security, data protection, accuracy and integration questions that do not arise with ordinary software. Most organisations run it as a structured review with pilots, stakeholder sign off and defined exit conditions.
Primary research · August 2026
How ChatGPT Shortlists Software Brands
An audit across 10 categories and 60 buying questions. I recorded what ChatGPT reads, throws away and links to when a buyer asks it which software to buy, and what that decides.
- 60
- Questions asked
- 10
- Software markets
- 2,680
- Results read
- 367
- Links shown
Free download
Get the full report
35 pages · PDF · 536 KB. Enter your details and it downloads straight away.