An AI agent is software that works toward a goal by interpreting context, choosing actions, and using connected tools or data. Unlike a chatbot that mainly replies to prompts, an agent can carry a task across multiple steps. But the label alone does not tell you how autonomous or reliable it is: companies are experimenting and deploying agents, while large-scale use remains uneven.
What are AI agents?
An AI agent is a goal-directed software component that can interact with its environment, gather information, and take actions to pursue a defined outcome. Amazon Web Services describes an agent as a program that can interact with its environment, collect data, and use it to perform self-directed tasks toward predetermined goals (AWS: What are AI Agents?).
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A practical way to understand the loop is:
- Receive a goal, such as investigating a support issue or preparing a report.
- Read relevant context, including user instructions, documents, or application data.
- Select a next step and use a connected tool, such as a search system, database, or business application.
- Inspect the result, then continue, stop, or ask a person to review or decide.
Common components include an AI model, instructions, connections to tools or APIs, stored state or memory, and safeguards. A larger agentic system may also add orchestration, identity, policies, evaluation, or multiple agents. These are design patterns, not proof that a system will reason correctly, learn from every interaction, or act without supervision. AWS calls an agent an “individual, goal-directed component” (AWS: Introduction to AI agents).
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How are AI agents different from chatbots?
A conventional chatbot is primarily designed to respond to a user’s message. An agent can use that response as one step in a broader task: retrieve information, call a tool, examine the result, and decide what to do next. The difference is not simply whether a system uses a language model; it is whether it can take actions toward a goal through connected systems.
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“Agent” is used inconsistently. Some counts include embedded agent features and pilots; others refer to fully autonomous systems operating without human oversight at each step. To judge a product, ask what actions it can take, what permissions it has, when a person must approve or intervene, and how failures are handled.
What can AI agents do at work?
Reported areas of use include coding and software development, IT and knowledge-management tasks, research and reporting, data analysis, customer service, and internal process automation. Organizations may use a mixture of copilots, built-in product features, pilots, and agents with varying levels of human supervision; the examples do not imply autonomous end-to-end operation.
A late-2025 survey of more than 500 US technical leaders by Anthropic and Material reported agent use in multi-stage workflows, including coding assistance, data analysis and reporting, research, customer service, and internal process automation (Anthropic: Enterprise). These are reported use cases and expectations, not independent proof that every deployment produces a return.
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Are companies actually using AI agents?
Yes, but survey results differ because they ask different questions of different groups. “Piloting,” “experimenting,” “scaling,” and “fully autonomous” are not interchangeable measures.
| Survey and population | Reported finding | How to interpret it |
|---|---|---|
| Gartner, 2025: 360 IT application leaders at organizations with 250 or more employees in North America, Europe, and Asia-Pacific; surveyed in May and June | 75% said their organization was piloting, deploying, or had deployed some form of AI agent; 15% said it was considering, piloting, or deploying fully autonomous AI agents. | The two figures use different categories and autonomy thresholds. They are not competing estimates of the same thing. (Gartner survey) |
| McKinsey & Company, 2025: 1,993 participants at all organizational levels; fielded June 25–July 29 | 23% said their organization was scaling an agentic AI system somewhere, and 39% said it had begun experimenting. | Scaling usually covered only one or two functions; no individual function had more than 10% of respondents reporting scaled use. (McKinsey: The state of AI) |
| Anthropic and Material, 2026 report based on a late-2025 survey of more than 500 US technical leaders | 57% said their organization deployed agents for multi-stage workflows, including 16% across multiple teams. | This narrower technical-leader sample and its workflow definition make it unsuitable for direct comparison with the Gartner or McKinsey results. (Anthropic: Enterprise) |
The combined picture is interest and active experimentation, alongside uneven production scale and autonomy. None of these survey measures establishes one universal adoption rate.
Why do companies want agents?
The appeal is that an agent may carry work across several steps and applications rather than only generate a response. That can be useful when a task involves gathering context, applying a repeatable process, and producing an outcome that can be checked. The business case still depends on whether the specific workflow is important, measurable, and suitable for tool-mediated action.
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PwC’s May 2025 survey of 300 senior executives found that 79% said agents were already being adopted in their companies. Among those adopting, 66% said agents delivered measurable value through increased productivity. These are executives’ survey responses, not a controlled study showing that agents caused the reported gains. PwC also cautions that broad adoption does not necessarily mean deep impact or transformed workflows (PwC: AI Agent Survey).
What risks and deployment challenges should a company consider?
Because an agent can act through connected tools, permissions and review processes are part of the system design—not details to leave until after choosing a model.
Security, governance, and trust
In Gartner’s 2025 survey of IT application leaders, 74% believed agents represented a new attack vector. Only 13% strongly agreed their organization had suitable governance structures, and 19% reported high or complete trust in vendors’ hallucination protection. These are respondents’ views, not measured rates of attacks or system accuracy (Gartner survey).
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Integration, data, and change
Anthropic and Material’s late-2025 survey identified integration (46%), data quality (42%), and change management (39%) as scaling challenges among its respondents. Those figures describe reported obstacles, not universal failure rates (Anthropic: Enterprise).
Human oversight and recovery
Before deployment, decide which actions an agent may take on its own, which require approval, and when it must stop or escalate. Log actions and results so people can audit outcomes, and provide a way to recover from mistaken or incomplete actions. A system with access to sensitive records or consequential operations needs tighter permissions and review than one limited to drafting or summarizing.
How should a company evaluate an agent use case?
Start with the workflow, not the agent label. A practical evaluation should cover:
- Value and measurement: Is the workflow important, and can the organization measure a meaningful outcome?
- Task structure: Does the work genuinely involve multiple steps that software can handle, rather than a single answer?
- Data and integration: Is the relevant information reliable and accessible, and what systems must connect?
- Permissions and consequences: What can the agent read or change, and what is the cost of a mistake?
- Review and escalation: Which actions require human approval, and what should happen when the agent is uncertain or a tool fails?
- Monitoring and governance: How will performance, errors, access, and changes to the system be tracked over time?
These questions help distinguish a useful, bounded workflow from a demo that is difficult to govern or measure. They also make clear whether a company needs an agent at all, or whether a simpler automation or assistant would meet the need.
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