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Agentic AI in workplace collaboration tools is AI that can use workplace context and connected tools to pursue a goal by taking actions—not just answer a prompt or draft text. How much it can do varies: some agents suggest or prepare steps for a person to approve, while others can carry out bounded workflow actions. “Agentic” is a broad label, not a guarantee of a particular capability, level of autonomy, or reliability.
What is agentic AI?
Microsoft defines an AI agent as “a system that achieves a set goal by taking action based on the inputs it perceives in its environment.” In a workplace, that environment might include permitted organizational information, a conversation, and connected applications. An agent uses those inputs to work toward a requested outcome.
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The term is not a standardized technical category. The MIT 2025 AI Agent Index notes that definitions vary across fields and remain nebulous. For a practical assessment, look past the label and ask what a particular system can observe, decide, and do.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteHow are AI agents different from copilots?
The distinction is a continuum of delegated action, not a hard line between two kinds of software. A conventional assistant or copilot commonly responds to a prompt by answering a question, drafting a message, or summarizing content. More agentic behavior appears when a system is given an outcome, plans steps, uses tools, or carries out parts of a workflow.
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| Behavior | Typical role of the AI | Human involvement |
|---|---|---|
| Answering or summarizing | Responds to a prompt using available context. | A person asks, reviews, and decides what to do next. |
| Drafting or suggesting | Prepares content or recommends next steps. | A person reviews or approves before acting. |
| Planning and tool use | Selects steps or invokes connected tools to work toward an outcome. | Approval may be required for some or all consequential actions. |
| Bounded workflow execution | Carries out authorized actions, such as triggering a workflow or changing a record. | People set permissions and oversight; the system’s permitted actions depend on its implementation. |
These are illustrative levels, not a formal taxonomy. Even a system that plans or takes action does not have independent intent: it operates within its design, instructions, available context, and permissions. A person can remain responsible for planning and approving critical steps.
What can AI agents do in Microsoft Teams and other collaboration tools?
Microsoft documents agents that extend Copilot with specialized knowledge, automate workflows, and work in Microsoft Teams, Outlook, SharePoint, or custom apps. Its administration guidance describes uses such as answering questions, automating tasks, running business processes, offering suggestions, and providing insights. These are documented Microsoft use cases, not evidence that every collaboration product—or every agent within one product—supports the same functions.
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Two Microsoft agent approaches
- Declarative agents use Copilot’s AI infrastructure and can be configured with specialized knowledge or instructions.
- Custom-engine agents are built for more tailored workflows, rather than relying solely on Copilot’s AI infrastructure.
The practical difference for a team is not just where an agent appears in an app. It is what information it can use and what actions it can take. A tool that answers questions from permitted content has a different reach and risk from one that can also initiate a business process.
Are AI agents safe to use at work?
Safety depends on the agent’s access, action permissions, oversight, and management—not on the word “agent.” Before enabling an agent, teams can use Microsoft’s documented administration control areas as prompts for a governance review:
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- Data access: Which organizational content and connected sources can it access?
- Actions: Which tools can it invoke, and can it change records, send communications, or trigger workflows?
- Approval: Which steps require a person to review or approve them?
- Creation and distribution: Who can create, share, publish, assign, or deploy the agent?
- Monitoring: What visibility is available into the agent and the actions it takes?
- Removal: Who can disable, block, or remove it if its access or behavior is no longer appropriate?
These questions reflect control areas described in Microsoft’s admin guidance; they are a practical checklist, not a claim that one universal governance standard mandates this exact list. Microsoft documents approval, publishing, deployment, removal, and blocking controls, as well as additional Power Platform admin settings for agents built with Copilot Studio. Available controls depend on the product and the way an agent was built and deployed.
How should teams evaluate an agent before using it?
Compare the specific implementation rather than relying on a product’s “agentic” marketing label. Useful criteria include:
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- Autonomy and approval: Which steps does the agent choose or execute, and where must a person approve?
- Context and reach: Which collaboration apps, data sources, and organizational content can it use?
- Action space: Does it only answer and draft, or can it also change records, trigger workflows, or communicate?
- Governance: What admin visibility and control exists for access, sharing, publishing, deployment, and removal?
- Evidence and evaluation: What limitations, safety evaluation, monitoring, and human oversight does the vendor disclose?
The MIT AI Agent Index examines agent action spaces and available safety information, while Microsoft’s documentation describes its own integrations and management controls. Neither source establishes a universal ranking of workplace agents. OECD’s 2026 landscape report also cautions that its findings are indicative rather than exhaustive because adoption evidence is limited and some data are self-reported. It does not establish a directly comparable workplace productivity result, so claims of productivity gains should be assessed on their own evidence.
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Sources and scope
- Microsoft, AI Agent FAQ: Definitions and Explanations.
- MIT AI Agent Index research team, The 2025 AI Agent Index, FAccT ’26 (June 2026).
- Microsoft Learn, Agents FAQ for Microsoft 365.
- Microsoft Learn, Agents admin guide for Microsoft 365.
- OECD, The Agentic AI Landscape and Its Conceptual Foundations (2026).
The product examples and governance details here are strongest for Microsoft 365. The cited sources do not provide a current feature-by-feature comparison with Google Workspace or Slack, so equivalent support or controls in those products should not be assumed.
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