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What is agentic AI?
“Agentic AI” describes AI systems that do more than generate a response: they can work toward a goal by taking actions, using information from what happens, and deciding what to do next. NIST’s overview describes such systems as capable of making decisions, learning from interactions, and adapting to changing environments. Definitions differ, however; the OECD’s February 2026 paper examines the term’s varying foundations rather than declaring a single threshold everyone uses.
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For practical evaluation, call a system agentic to the extent that it can pursue a goal through selected actions, interact with tools or interfaces, observe results, and adjust its next step without the user specifying every move. This is an operational test, not an official certification or universal definition. An agent can be narrowly capable, unreliable, or tightly supervised; the label does not imply general intelligence or open-ended independence.
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What does AI have to do to be agentic?
Look for an observable action loop, not a product name. NIST describes agent systems as combining general-purpose models with software scaffolding that lets the model use tools to do more than generate text.
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- Work toward a goal spanning steps. The system must be able to pursue an outcome, rather than only answer one prompt.
- Select or sequence actions. It can choose a tool or step and revise its approach when circumstances or results change.
- Interact with an environment. It can read from or write to software, a service, a device, or another system.
- Inspect feedback. It can use the outcome of an action to inform what it does next.
- Exercise some delegated discretion. It can proceed through at least part of the task without the user directing every action.
- Operate within defined permissions and oversight. The system’s practical autonomy depends on its access, allowed actions, monitoring, and approval checkpoints.
A system that only drafts a plan but cannot carry out or verify any step may be useful, but it demonstrates less agency than one that can execute a bounded workflow and react to the outcome.
How is an AI agent different from a chatbot or a fixed script?
A chatbot ordinarily responds to a user turn with text. An agentic system may continue through a cycle: plan, call a tool, inspect the result, and decide whether to continue, change course, or ask for help. A conventional script can also take actions, but usually follows predetermined rules. The key distinction is whether the system can select or adapt steps toward a goal that has not been specified as a complete sequence.
Consider a calendar request: “Find a time next week when everyone is available and prepare an invitation.” A chatbot might explain how to do that. A fixed script might check one calendar and send an invite according to preset rules. An agentic workflow could inspect calendars, propose a time, prepare the invitation, check the result, and pause for approval before sending it. Whether that last step is permitted is a matter of system design and access—not proof that every agent can safely handle calendars.
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NIST’s February 17, 2026 announcement lists writing and debugging code, managing email and calendars, and shopping as emerging agent use cases. These examples illustrate tasks involving external systems; they do not establish that every system can perform them reliably.
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Can AI agents actually take actions on their own?
They can take actions within the permissions and workflow they have been given. “On their own” is not an all-or-nothing condition: an agent might search information without approval, draft a change, then require a person to authorize sending, deleting, purchasing, or deploying it.
Read-only access generally lets a system inspect information but not change it. Write access can let it alter files, send messages, modify code, or act on accounts and devices. That makes the difference between a harmless mistake and a consequential one depend partly on what tools are connected and what those tools permit. Reversibility matters too: a draft can be discarded, while a sent message or physical action may be difficult or impossible to undo.
An OECD.AI article published September 24, 2026 reports practitioner interviews across 25 organisations in 11 countries. None of those participating organisations reported deploying agentic AI with unrestricted autonomy. Interviewees described structured tasks, outcomes that could be checked, bounded error costs, and human checkpoints before consequential actions. This is evidence from a defined interview sample, not a census of all deployments.
What should you check before trusting an agentic AI claim?
Compare systems by the practical boundaries and evidence of their behavior, not by how confidently they use the word “agent.” NIST’s tool-use taxonomy raises functionality, access, risk, reliability, monitoring, and autonomy; OECD practitioner accounts add controls such as validation, least-privilege access, sandbox testing, traceability, and human checkpoints.
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| What to compare | What to ask |
|---|---|
| Task scope | Is the system limited to a narrow workflow, or can it pursue a broader goal? |
| Planning | Can it break down a task and revise its sequence when a step fails? |
| Tool access | Which applications, data, services, or devices can it reach? |
| Permissions | Is access read-only, constrained write, or unrestricted write? |
| Oversight | Which actions require approval? Can it pause and ask for clarification? |
| Reversibility | Can an action be undone, or could it have lasting effects? |
| Reliability | How consistently does it use tools correctly and complete the task? |
| Monitoring and traceability | Can a user inspect the sequence of actions, tool calls, and outcomes? |
| Recovery | Can it stop safely, explain a failure, and recover without compounding it? |
Ask for a demonstration on a realistic task: what the system observes, which action it chooses, what happens when a tool fails, and where it stops for approval. A polished answer about capabilities is not a substitute for seeing the permitted action path and its controls.
What risks and standards questions remain?
When an AI output can trigger an action, an error can move beyond incorrect text into an incorrect change. The OECD.AI practitioner account reports concerns including hallucinations, incorrect tool use, variable behavior across contexts and runs, accountability, and failures that are harder to trace across multi-agent or cross-organisational workflows. Interviewees also raised agent hijacking, credential theft, data leakage, and other attack surfaces.
NIST’s August 2025 discussion recommends assessing whether a tool can reach external resources, whether it has write permissions, the severity and reversibility of possible effects, the reliability of both tool and model, and whether actions can be monitored. Its taxonomy helps reason about capabilities and risks; it is not a safety certification for a particular product.
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In short, the label earns meaning when a system demonstrably selects and adapts actions toward a goal, uses tools to affect an environment, and responds to outcomes. Its real-world agency—and risk—are bounded by its permissions, reliability, monitoring, and human checkpoints.
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