Artificial intelligence has evolved from systems that simply respond to instructions into systems that can increasingly plan, use tools, interact with software and take actions to achieve a goal.

These systems are commonly called AI agents.

An AI agent is a software system that can receive an objective, reason about the steps needed to accomplish it, interact with its environment and perform actions with varying degrees of autonomy. NIST describes agents as software programs that can interact with their environment, receive information and undertake self-directed actions in service of an externally specified goal.

The concept has become particularly important as generative AI and large language models have become capable of interacting with external tools, databases, applications and digital environments.

But what exactly makes an AI system an agent?

What Is an AI Agent?

An AI agent is an artificial intelligence system designed to pursue a goal by observing its environment, reasoning about available information, deciding what to do and taking actions.

Unlike a conventional chatbot that primarily generates an answer to a prompt, an AI agent can potentially perform a sequence of actions.

For example, a user could ask an AI agent:

“Find three suitable flights, compare their prices and schedules, and prepare the best option for me.”

A conventional AI system might provide information about how to search for flights.

An AI agent could potentially:

1. Understand the objective.
2. Break the task into smaller steps.
3. Search available information.
4. Compare results.
5. Apply user-defined criteria.
6. Select or recommend an option.
7. Interact with another application if authorized.
8. Report the completed work.

The important difference is therefore not simply that an AI agent is “smarter.”

The difference is its ability to connect reasoning with action.

How Does an AI Agent Work?

A typical AI agent combines several components rather than relying on an AI model alone.

Google Cloud describes key agent capabilities including reasoning, acting, observing, planning, collaboration and self-refinement.

A simplified AI agent architecture can be represented as:

Goal → Observe → Reason → Plan → Use Tools → Act → Observe Results → Continue or Finish

The process may repeat several times before the agent completes its task.

1. Goal

Everything starts with an objective.

The goal could be:

• Research a topic
• Analyze financial data
• Write software
• Monitor a system
• Organize information
• Search documents
• Automate a business process
• Interact with another application

The agent needs to understand what successful completion means.

2. Observation

The agent needs information about its environment.

Depending on the system, this could include:

• User messages
• Documents
• Databases
• Websites
• APIs
• Application interfaces
• Sensor data
• Files
• Previous interactions

NIST's research on agent tool use emphasizes that agents can perceive environments and use tools to interact with them.

3. Reasoning

The AI model evaluates the available information and determines what should happen next.

A large language model may serve as the reasoning component, while other software components provide memory, tools and access to external systems.

4. Planning

Complex objectives often need to be divided into smaller tasks.

For example:

Goal: Prepare a market analysis.

The agent might create a sequence such as:

Find data → verify sources → analyze data → identify trends → prepare report

Planning allows the system to work through a multi-step process instead of producing a single response.

5. Tool Use

This is one of the most important characteristics of modern AI agents.

An agent may be connected to tools such as:

• Web search
• APIs
• Databases
• Code execution
• File systems
• Email systems
• Calendars
• Enterprise applications
• Computer interfaces

NIST's research identifies tools for perception, planning, memory, authentication, computer use, code execution, APIs and other forms of interaction.

This means an AI agent can potentially move beyond generating text and interact with the digital environment.

6. Action

After deciding what to do, the agent performs an authorized action.

For example, it could:

• Retrieve information
• Update a database
• Run code
• Create a document
• Call an API
• Search a website
• Schedule an event
• Interact with software

The level of autonomy depends on how the system has been designed and what permissions it has.

AI Agent vs AI Assistant vs Chatbot

These terms are often used interchangeably, but they are not necessarily the same.

Google Cloud similarly distinguishes AI agents from assistants and bots by differences in autonomy, complexity and interaction patterns.

However, these categories are not absolute.

An “assistant” can contain agentic capabilities, while an AI agent can be designed with very strict limits and human approval requirements.

The important variable is how much authority the system has to decide and act.

What Is Agentic AI?

Agentic AI refers to AI systems designed to operate with greater autonomy, including the ability to pursue goals, make decisions and interact dynamically with users, software or environments.

NIST's current description of agentic AI emphasizes autonomous decision-making, adaptation and interaction with changing environments.

The relationship can be simplified as:

AI → Generative AI → AI Agents → Agentic AI Systems

These categories overlap, so they should not be treated as rigid technical classifications.

An AI agent can be one component inside a larger agentic AI system.

What Makes AI Agents Different From Traditional AI?

Traditional AI systems can be designed to perform a particular task:

Input → Model → Output

An agentic system can introduce an additional loop:

Goal → Observe → Reason → Plan → Act → Observe → Adjust → Act

This creates a more dynamic system.

For example, an AI model can generate computer code.

An AI coding agent could potentially:

1. Understand a software requirement.
2. Inspect an existing codebase.
3. Identify relevant files.
4. Write code.
5. Run tests.
6. Analyze errors.
7. Modify the code.
8. Run the tests again.
9. Prepare the resulting changes.

NIST specifically notes that current AI agents can perform tasks such as building software applications and browsing the internet.

The Role of Large Language Models

Large language models are often used as the central reasoning or language component of AI agents.

The model can help the agent:

• Understand natural-language goals
• Interpret information
• Generate plans
• Select tools
• Process tool results
• Communicate with users

But an LLM alone is not necessarily an AI agent.

A useful distinction is:

LLM = Model

AI Agent = Model + Instructions + Tools + Memory + Control Logic + Environment Access

The exact architecture varies between systems.

Memory in AI Agents

Memory can help an agent maintain information across multiple steps or interactions.

Different systems can implement different types of memory, including:

Short-Term Memory

Information relevant to the current task or conversation.

Long-Term Memory

Information retained for future interactions.

Working Memory

Temporary information needed while completing a task.

External Memory

Information retrieved from databases, documents or other external sources.

Memory becomes especially important when an agent has to perform long or complex workflows.

Tools Give Agents Their Practical Power

One of the biggest differences between a simple AI model and an agent is access to tools.

Consider a language model that is asked:

“Check this company's latest sales data.”

Without access to external data, it can only respond based on information available to it.

An agent connected to an authorized business database could potentially retrieve the relevant records, analyze them and produce a report.

This creates a new security boundary.

The AI is no longer merely generating information.

It may be interacting with real systems.

NIST has therefore been studying tool-use architectures and the security implications of giving agents different levels of access, including read-only and write permissions.

What Are AI Agents Used For?

AI agents can be applied across many areas.

Software Development

Agents can help analyze code, generate changes, run tests and troubleshoot errors.

Research

Research agents can search information, compare sources and organize findings.

Business Operations

Agents can interact with enterprise systems to automate repetitive workflows.

Customer Service

Agents can potentially handle multi-step customer requests instead of simply answering questions.

Cybersecurity

Agents can monitor information, investigate alerts and assist security teams.

Finance

Agents can analyze financial information and automate certain workflows where appropriate controls exist.

Manufacturing

Agents can potentially interact with industrial software, production information and monitoring systems.

Personal Productivity

Agents can work with calendars, documents, email and other productivity tools when given appropriate permissions.

The potential applications are broad because the same underlying architecture can be connected to different tools and environments.

AI Agent Security Risks

Greater autonomy also creates new security challenges.

An AI model that only generates text has one type of risk.

An AI system that can access databases, execute code or interact with external applications has a much larger attack surface.

NIST's 2026 work on AI agent security identifies risks including indirect prompt injection, data poisoning, insecure models and harmful actions resulting from poorly aligned objectives.

Other important security considerations include:

• Excessive permissions
• Unauthorized tool use
• Sensitive-data exposure
• Identity and authorization problems
• Malicious instructions in external content
• Unintended actions
• Poor monitoring
• Inadequate human oversight

NIST's May 2026 analysis of responses concerning AI-agent security found broad agreement that agents create novel security concerns and that conventional cybersecurity practices may need adaptation.

Why Identity and Authorization Matter

One of the emerging questions is:

Who is responsible when an AI agent takes an action?

If an agent sends an email, changes a database record or accesses confidential information, the system needs to know:

• Which agent performed the action?
• Who authorized it?
• What permissions did it have?
• What data did it access?
• What tools did it use?
• What happened afterward?

NIST launched work in 2026 examining identity and authorization for software and AI agents, including identification, authorization, auditing and non-repudiation.

This is likely to become an increasingly important part of enterprise AI infrastructure.

NIST's AI Agent Standards Initiative

The growing importance of AI agents has already led to standards-related work.

In February 2026, NIST announced its AI Agent Standards Initiative, focusing on secure and interoperable AI-agent ecosystems. The initiative includes work around standards, open-source protocols, agent security and identity.

This is significant because AI agents need to communicate not only with people, but increasingly with:

software → APIs → databases → applications → other agents

Interoperability could therefore become an important part of the next stage of AI development.

What Is a Multi-Agent System?

Some AI applications can use multiple agents rather than a single agent.

For example:

Research Agent → Analysis Agent → Writing Agent → Review Agent

Each agent can have a different role.

One agent might collect information while another analyzes it and another prepares the final output.

This approach is known as a multi-agent system.

It can potentially make complex workflows easier to divide, although coordination, security and reliability become additional challenges.

Are AI Agents Fully Autonomous?

Not necessarily.

Autonomy exists on a spectrum.

An agent can be designed to:

Low autonomy

Ask the user before every important action.

Moderate autonomy

Perform routine steps automatically but request approval for sensitive actions.

High autonomy

Complete long sequences of actions with limited human intervention.

The appropriate level depends on the task.

For example, an agent preparing a draft report might operate with substantial autonomy.

An agent capable of transferring money, deleting production data or modifying critical infrastructure would require much stronger controls.

The Future of AI Agents

AI agents are moving the discussion around artificial intelligence from:

“Can AI generate an answer?”

toward:

“Can AI accomplish a task?”

That distinction is important.

The next generation of AI systems is increasingly being designed to combine:

Reasoning + Planning + Memory + Tools + APIs + Data + Action

NIST's current work shows that standards, security, identity and measurement are becoming important alongside the development of agent capabilities.

The technology is still developing, and there is no single architecture that defines every AI agent.

What does appear increasingly clear is that AI agents represent a shift from AI that primarily generates outputs toward AI systems that can participate in multi-step workflows and take actions within defined environments.

Frequently Asked Questions

What is an AI agent?

An AI agent is a software system that can pursue a goal by observing information, reasoning, planning and taking actions, often with access to external tools.

Is ChatGPT an AI agent?

A conversational AI model and an AI agent are not necessarily the same thing. A system can become more agentic when it gains capabilities such as planning, tool use, memory and authorized actions.

What is the difference between AI and AI agents?

AI is the broader field. An AI agent is a system designed to use AI capabilities to pursue goals and perform actions within an environment.

What is Agentic AI?

Agentic AI describes AI systems with greater autonomy in decision-making, planning and interaction with their environment.

Are AI agents safe?

AI-agent safety depends on architecture, permissions, monitoring, security controls and human oversight. NIST identifies several security risks specific to agent systems.

Can AI agents use the internet?

Yes, an agent can be designed with authorized web-access tools. However, external web content can introduce security risks such as indirect prompt injection, so access needs appropriate controls.

Can AI agents work together?

Yes. Multiple agents can be designed to collaborate on different parts of a larger workflow.

Key Takeaway

An AI agent is more than an AI model that generates an answer.

It is a system capable of combining goals, reasoning, planning, memory, tools and actions to complete tasks with varying levels of autonomy.

As AI agents become more capable, the technical challenge is no longer only about improving the underlying model. It is also about building systems that are secure, reliable, observable, interoperable and appropriately authorized.

That is why AI agents are becoming an important part of the broader evolution from generative AI toward agentic AI systems.

• Sources

  1. NIST — AI Agent Standards Initiative
     https://www.nist.gov/news-events/news/2026/02/announcing-ai-agent-standards-initiative-interoperable-and-secure
  2. NIST — Agentic AI
     https://www.nist.gov/agentic-ai
  3. Google Cloud — What Are AI Agents?
     https://cloud.google.com/discover/what-are-ai-agents
  4. Google Cloud Documentation — Generative AI Glossary
     https://docs.cloud.google.com/docs/generative-ai/glossary