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What makes AI agentic?
Anthropic defines an agent as “an AI model that directs its own processes and tool use when accomplishing a task—that is, deciding for itself how to achieve what users want, rather than following a fixed script.” In practice, Anthropic describes a loop: “it plans, acts, observes the result, adjusts, and repeats until the task is done or it needs to check in for human input.” (Anthropic, Trustworthy agents in practice, April 9, 2026.)
The defining feature is not a special label or a more natural-sounding conversation. It is the system’s ability to choose and carry out multiple steps toward a goal using tools. A system that only drafts a response for you to copy is doing something different from one that can open a calendar, propose a change, and make it after you approve.
How is an agent different from a copilot?
“Copilot” is used for products that assist people, and the label alone does not tell you how much a system can do. In a typical chat interaction, you ask a question, receive a response, and choose the next step yourself. With an agent, you may assign an outcome and let the system handle parts of the work across connected apps, checking in at milestones or when it reaches a decision it should not make alone. Microsoft uses this distinction in its description of Copilot Cowork.
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| Aspect | Prompt-and-response assistance | Agent-style delegation |
|---|---|---|
| Starting point | You ask for an answer, draft, or recommendation. | You specify a goal or outcome. |
| Next steps | You decide what to do next and usually perform the action. | The system can select steps, use enabled tools, and continue based on what happens. |
| Human role | You guide each turn and act on the result. | You set the scope, review consequential actions, and handle decisions the system returns to you. |
| What to check | Whether the answer is accurate and useful. | Whether the answer is sound and whether the system’s actions, access, and checkpoints are appropriate. |
These are ends of a spectrum, not rigid product categories. A copilot may include agent-like features, and an agent may still need frequent human direction.
What work can an agent do?
Tasks that cross several steps
Anthropic illustrates delegation with expense receipts: an agent can transcribe receipt images, extract amounts and vendors, categorize expenses, and enter them in a company system. If it encounters a policy question, it can pause and ask for human input. The example shows how a task can be broken into steps with a hand-back point; it is not evidence of a particular accuracy rate.
Work across apps and data
Microsoft describes Copilot Cowork as a system for planning and executing long-running, multi-step work across apps, files, and data. Its examples include preparing communications and documents, research, scheduling, and handling calendar changes after approval. These are vendor-described capabilities, not a guarantee that every task will work for every user or account. Check Microsoft’s Copilot Cowork information for current availability and licensing.
More generally, agents can use tools such as browsers, email, calendars, files, code execution, APIs, software extensions, or even systems involving human or physical interaction. The available tools shape the agent’s practical capabilities as much as the underlying model does. Giving an agent access to a calendar is not the same as letting it send emails, change records, or run code.
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Assess the agent by what it can do in its particular setup, not by the word “agent.” NIST’s discussion of tool use in agent systems offers useful dimensions for evaluating that setup: functionality, access, risk and reversibility, reliability, monitoring, and autonomy. These are evaluation lenses, not a finalized universal classification.
- Access: Is the agent read-only, allowed to make narrowly constrained changes, or able to write and act without those limits? Start with the least access needed for the task.
- Environment: Is it working with trusted internal data, or could it encounter content from an open website, email, or document that should not be treated as an instruction?
- Consequences and reversibility: Could a mistake send a message, spend money, delete a file, or alter a record? Can the action be undone?
- Monitoring: Can you see the plan, actions taken, and results? Is there a way to stop the task or recover if a step fails?
- Reliability: What happens when the agent misunderstands the request, receives unexpected information, or cannot complete a step? Is a partial result clearly reported?
NIST’s examples distinguish read-only access from constrained or unrestricted write access, and trusted from untrusted environments. Those distinctions help explain why the same broad assignment can be low-risk in one configuration and high-risk in another. Read more in NIST’s August 2025 discussion of tool use in agent systems.
Where do approval checkpoints and safeguards fit?
Human control can be built into the workflow. An agent might show a proposed plan before starting, require approval before sending a message or changing a record, or stop when a policy or ambiguous choice requires judgment. A good checkpoint is attached to a consequential decision; requiring approval for every routine step can make delegation cumbersome without necessarily making the important decisions clearer.
Anthropic recommends considering the model alongside its harness—the instructions and guardrails around it—its tools, and the environment it operates in. Its practical controls include deciding which tools are available and whether actions are permitted, require approval, or are blocked. This matters because a capable model can still be exposed to risk by overly broad permissions or an unsafe environment.
Prompt injection is one such risk: hostile or misleading content encountered in a document or on a website may try to steer an agent away from the user’s intent. Misunderstanding a request is another. OpenAI’s computer-using-agent page gives examples of possible mistakes, from a typo in an email to buying the wrong item or deleting a document, and describes mitigations at the model, system, and post-deployment levels. These examples illustrate possible consequences; they do not establish how often such errors occur. Safeguards can reduce risk, but they do not make an agent error-proof.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What does agentic AI mean for work and standards?
Agent tools can change how people delegate tasks, but the sources cited here do not establish economy-wide productivity gains, job displacement, or a comparative reliability rate. Vendor examples describe what a product is designed to do; they should not be read as independent evidence that it will complete a task accurately in your organization.
Interoperability, identity, and security are still active standards concerns. In February 2026, NIST announced an AI Agent Standards Initiative with three areas of work: industry-led standards, community-led open-source protocol work, and research on security and identity. NIST said reliability and interoperability constrain agents’ real-world utility. The initiative is work underway, not a completed universal standard. See NIST’s announcement.
For an organization evaluating an agent product, compare the tasks and apps it supports, the permissions and approval controls available, how data is handled, what administrators can monitor, how failures are recovered from, licensing, and interoperability. A polished demonstration does not answer those deployment questions.
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