For years, the most common way to interact with artificial intelligence was through a chatbot.

You typed a question.

The AI generated an answer.

You asked another question.

The AI responded again.

That model of interaction is now changing.

A new generation of AI systems is being designed to do more than generate text. These systems can potentially plan tasks, use tools, interact with software, search for information, write and execute code, analyze files, make decisions and continue working across multiple steps.

They are generally described as AI agents.

The difference may sound small, but it represents a major change in how AI software is built.

A traditional chatbot primarily follows:

User
 ↓
Prompt
 ↓
AI model
 ↓
Answer

An AI agent can follow something closer to:

User
 ↓
Goal
 ↓
Planning
 ↓
Reasoning
 ↓
Tool selection
 ↓
Action
 ↓
Observation
 ↓
More reasoning
 ↓
More actions
 ↓
Completed task

The AI is no longer simply responding to a question.

It is attempting to complete a goal.

---

What Is an AI Agent?

An AI agent is a software system that uses an AI model to decide and execute actions toward a particular objective.

The exact definition varies across the industry, but an agent generally combines several components:

• an AI model
• instructions or goals
• tools
• memory or state
• planning
• an execution loop
• feedback from the environment

A simplified architecture looks like this:

                  User Goal
                     ↓
                AI Model
                     ↓
                  Planner
                     ↓
              Tool Selection
                     ↓
        ┌────────────┼────────────┐
        ↓            ↓            ↓
      Search        Code         APIs
        ↓            ↓            ↓
        └────────────┼────────────┘
                     ↓
                 Observation
                     ↓
                 AI Model
                     ↓
              Next decision

This is different from simply asking a language model to produce a response.

The model becomes part of a larger system that can interact with an external environment.

Our earlier guide on AI agents and how they work covers the basic architecture in more detail.

---

The Chatbot Model Is Mostly Reactive

Traditional chatbots are generally reactive.

The user starts the interaction.

User:
Explain how GPUs work.

AI:
A GPU is...

The user then asks another question.

User:
How are GPUs used for AI?

AI:
GPUs are used...

The system waits for the next instruction.

This is useful for:

• answering questions
• summarizing documents
• explaining concepts
• generating text
• translating
• brainstorming

But it becomes less useful when the task involves many separate actions.

Imagine asking:

Research five competitors, compare their pricing, organize the results in a spreadsheet and summarize the differences.

A chatbot can generate instructions for doing this.

An agent can potentially perform much of the workflow itself.

---

Agents Turn Prompts Into Workflows

The biggest change is the transition from answer generation to task execution.

Consider a traditional request:

User:
Find the best laptop for programming.

A chatbot might explain what specifications to look for.

An agent could potentially:

Understand requirements
       ↓
Search products
       ↓
Collect specifications
       ↓
Compare prices
       ↓
Filter unsuitable models
       ↓
Create comparison
       ↓
Present results

The AI therefore becomes an orchestrator.

It decides what action should happen next based on the current state of the task.

---

The Agent Loop

Most agentic systems can be understood through a repeated loop.

          ┌──────────────┐
          │    Goal      │
          └──────┬───────┘
                 ↓
          ┌──────────────┐
          │   Reason     │
          └──────┬───────┘
                 ↓
          ┌──────────────┐
          │ Choose tool  │
          └──────┬───────┘
                 ↓
          ┌──────────────┐
          │ Take action  │
          └──────┬───────┘
                 ↓
          ┌──────────────┐
          │ Observe      │
          │ result       │
          └──────┬───────┘
                 ↓
          ┌──────────────┐
          │ Need more?   │
          └──────┬───────┘
                 │
          Yes ───┘
                 ↓
              Reason

This loop is one of the most important concepts in agentic AI.

The system does not necessarily know every step before it starts.

It can decide what to do next based on what happened in the previous step.

---

Tools Are What Make Agents Different

A language model by itself mainly generates outputs.

An agent can be connected to tools.

These tools might include:

• web search
• databases
• APIs
• calculators
• code execution
• file systems
• browsers
• enterprise software
• email systems
• calendars
• cloud infrastructure

For example:

AI Model
   │
   ├── Web search
   ├── Database
   ├── Calculator
   ├── Code interpreter
   ├── Browser
   └── External APIs

The model decides when a tool is useful.

This changes the role of the AI.

Instead of only saying:

“Here is how you could check the database.”

The system can potentially call the database itself.

---

AI Agents Can Use Computer Interfaces

One of the more important developments is computer-use capability.

Instead of interacting only with APIs, an AI system can potentially interact with graphical interfaces.

The workflow might look like:

AI
 ↓
Open browser
 ↓
Navigate to website
 ↓
Read page
 ↓
Click button
 ↓
Enter information
 ↓
Submit form
 ↓
Read result

This is significant because much of the world’s software is built around graphical interfaces.

If AI can reliably interact with those interfaces, an agent does not necessarily need a custom API for every application.

The computer itself can become the interface.

This direction is being pursued by major AI companies. OpenAI, for example, includes computer-use capabilities in its current model and agent tooling.

---

Coding Agents Are One of the Clearest Examples

Software development is particularly suitable for agentic AI because programming already involves a structured feedback loop.

A traditional coding assistant might generate:

function calculateTotal(items) {
    ...
}

The developer then copies the code into a project and tests it.

A coding agent can potentially do much more:

Understand task
      ↓
Inspect codebase
      ↓
Find relevant files
      ↓
Modify code
      ↓
Run tests
      ↓
Read errors
      ↓
Fix code
      ↓
Run tests again
      ↓
Review changes

Our article on how AI coding assistants generate code explains the underlying technology behind AI-assisted programming.

Agentic coding takes the concept further by allowing the system to operate across the development environment.

This is one reason coding has become one of the major testing grounds for AI agents.

---

Agents Need More Than a Powerful Model

It is tempting to think that building an agent simply means connecting a large language model to a few tools.

In reality, the engineering problem is much larger.

A useful agent may need:

AI model
+
Tool system
+
Memory
+
State management
+
Planning
+
Permissions
+
Error handling
+
Observability
+
Security

The model is only one component.

This is why two applications using the same underlying model can behave very differently.

One may be a simple chatbot.

Another may be a sophisticated agentic system.

---

Memory Gives Agents Continuity

A chatbot conversation already provides some short-term context.

But agents may require more persistent state.

For example, imagine an agent managing a long software project.

It may need to remember:

• what it has already changed
• which tests failed
• what files it inspected
• what the user requested
• which decisions were made
• what remains unfinished

This can be represented as:

Task
 ↓
State
 ↓
Action
 ↓
Result
 ↓
Updated state
 ↓
Next action

Memory can therefore become an important part of agent architecture.

However, memory is not the same as simply giving a model a larger context window.

Context is information currently available to the model.

Memory can involve storing and retrieving information across separate interactions.

Our article on AI model context windows explains the difference between context capacity and the information available during a model request.

---

Long Context Helps Agents Work With More Information

Agentic systems often need to process large amounts of information.

For example, a coding agent may need to understand:

Large codebase
   ↓
Multiple files
   ↓
Documentation
   ↓
Test results
   ↓
Error logs

A longer context window can make it easier to provide relevant information to the model.

Modern frontier models increasingly support very large context windows, including context capacities around the million-token range.

But a larger context window does not automatically solve the problem.

The system still needs to identify which information is relevant.

That is why agents often combine:

Context
+
Retrieval
+
Memory
+
Tool results

---

Agents Are Increasingly Using Retrieval

An agent can retrieve information when it needs it rather than receiving everything at the beginning.

For example:

User question
     ↓
Agent identifies missing information
     ↓
Search / database
     ↓
Relevant information
     ↓
Reasoning
     ↓
Answer or action

This approach is closely related to Retrieval-Augmented Generation, or RAG.

Our article on RAG vs fine-tuning explains why retrieval can be useful when an AI system needs access to external or changing information.

For agents, retrieval is particularly useful because the system can decide when information is needed.

---

Agents Can Combine Multiple Tools

A powerful agent does not necessarily rely on one tool.

Imagine an agent asked to prepare a market report.

It might use:

Web search
     ↓
Collect information
     ↓
Spreadsheet
     ↓
Calculate statistics
     ↓
Code execution
     ↓
Generate charts
     ↓
Document editor
     ↓
Final report

The AI model acts as the coordinator.

This is one of the biggest differences between a chatbot and an agent.

The chatbot produces an answer.

The agent can potentially coordinate an entire workflow.

---

Why AI Agents Need More Inference Compute

There is an important infrastructure consequence.

Agents can require many model calls for one user request.

A chatbot might perform:

Prompt → Model → Answer

An agent may perform:

Prompt
 ↓
Reasoning
 ↓
Search
 ↓
Model
 ↓
Read result
 ↓
Reasoning
 ↓
Code
 ↓
Model
 ↓
Test
 ↓
Reasoning
 ↓
Final answer

Every reasoning cycle can consume compute.

That means agentic AI can increase inference demand substantially.

The economics of agents therefore depend heavily on efficiency.

---

Agents Could Change AI Pricing

Traditional AI products often charge according to:

• subscription
• tokens
• API usage
• number of requests

Agentic applications introduce another possible unit:

completed work.

Consider the difference.

A chatbot:

100 AI requests

An agent:

10 completed tasks

Each task may require:

10–50 model calls
+
tools
+
search
+
code execution

This makes simple per-request pricing less representative of the actual computational work.

AI companies and application developers therefore have to think about the economics of the entire workflow.

---

Reliability Becomes More Important

A chatbot can produce a bad answer.

An agent can potentially take a bad action.

That difference is extremely important.

Consider:

Chatbot mistake
 ↓
Incorrect explanation

versus:

Agent mistake
 ↓
Incorrect API call
 ↓
Wrong database update
 ↓
Incorrect action

The consequences can be much greater.

This means agent systems need safeguards such as:

• permission controls
• action limits
• human approval
• validation
• sandboxing
• audit logs
• rollback mechanisms
• tool restrictions

The more authority an agent has, the more important these controls become.

---

Agents Need to Know When to Stop

Another difficult problem is termination.

Suppose an agent receives:

Fix the issue in this application.

How does it know when the task is finished?

It could:

Change code
 ↓
Run test
 ↓
Find another issue
 ↓
Change code
 ↓
Run test
 ↓
Find another issue
 ↓
Continue...

Without a reliable stopping condition, the agent could waste resources or make unnecessary changes.

A good agent therefore needs:

Goal
 ↓
Progress measurement
 ↓
Validation
 ↓
Completion criteria

This is much harder than generating a single response.

---

Agentic AI Can Fail in New Ways

Traditional language models can hallucinate information.

Agents introduce additional failure modes.

For example:

Wrong tool

The agent selects an inappropriate tool.

Wrong parameters

The agent calls a tool with incorrect information.

Bad planning

The agent chooses an inefficient sequence of actions.

Cascading errors

One incorrect action creates incorrect information for the next step.

Small error
    ↓
Wrong observation
    ↓
Wrong reasoning
    ↓
Wrong action
    ↓
Larger error

This is why agent reliability cannot be measured only by asking whether the model gives good answers.

The system has to be evaluated across the entire workflow.

---

Multi-Agent Systems Are Another Direction

Some AI systems use more than one specialized agent.

For example:

                 Main Agent
                     │
        ┌────────────┼────────────┐
        ↓            ↓            ↓
 Research Agent   Coding Agent   Review Agent
        │            │            │
        └────────────┼────────────┘
                     ↓
                Final result

One agent may search for information.

Another may write code.

Another may review the result.

The main agent coordinates them.

This can be useful for complex workflows, although it also increases system complexity and potentially increases compute usage.

More agents do not automatically mean better results.

The architecture has to justify the additional coordination.

---

Agents Are Moving Into Enterprise Software

Enterprise applications are particularly interesting for agentic AI because companies already have structured workflows.

For example:

Customer request
      ↓
CRM
      ↓
Database
      ↓
Internal knowledge base
      ↓
Approval system
      ↓
Email
      ↓
Final response

An agent could potentially coordinate these systems.

Instead of employees manually moving information between multiple applications, an AI system could perform parts of the workflow.

This is one reason enterprise software companies are increasingly adding agent functionality.

---

AI Agents Could Change Search

Search is another area where agentic AI could have a major effect.

Traditional search:

Question
 ↓
Search engine
 ↓
Results
 ↓
User chooses
 ↓
User reads

An agentic search system could potentially become:

Goal
 ↓
Search
 ↓
Read multiple sources
 ↓
Compare information
 ↓
Reason
 ↓
Search again if necessary
 ↓
Produce result

The user is asking for an outcome rather than simply receiving a list of links.

This does not mean traditional search disappears.

Different tasks require different interfaces.

But the distinction between searching for information and having AI perform research is becoming less clear.

---

Agents Are Moving Into Software Development

Software development may be one of the areas where agentic AI becomes especially visible.

A future development workflow could look like:

Developer
    ↓
Describe feature
    ↓
Coding agent
    ↓
Inspect repository
    ↓
Plan changes
    ↓
Modify code
    ↓
Run tests
    ↓
Open application
    ↓
Inspect output
    ↓
Fix problems
    ↓
Create pull request
    ↓
Developer review

The developer does not necessarily disappear from the workflow.

Instead, the developer’s role can shift toward:

• defining requirements
• reviewing changes
• setting constraints
• approving important actions
• testing the final result

The AI handles more of the repetitive execution.

---

Agents Could Become the Interface to Software

Today, humans learn how to use software interfaces.

You open an application.

You find the correct menu.

You fill out a form.

You click buttons.

An agentic system could eventually allow the user to describe the desired outcome instead.

Instead of:

Open application
 ↓
Find menu
 ↓
Fill form
 ↓
Click button

the interaction becomes:

User:
Update the customer record and send the revised invoice.

Agent:
Understands goal
 ↓
CRM
 ↓
Invoice system
 ↓
Email
 ↓
Confirmation

This could change the role of traditional user interfaces.

The interface may increasingly become a combination of:

natural language + software tools + AI agents.

---

But Agents Will Not Replace Every Chatbot

Chatbots remain useful.

If someone asks:

What is a semiconductor?

There is little reason to launch a complicated agent workflow.

A simple model can answer immediately.

The distinction can be represented like this:

Simple question
     ↓
Chatbot
     ↓
Answer


Complex task
     ↓
Agent
     ↓
Plan
 ↓
Tools
 ↓
Actions
 ↓
Result

The future is therefore unlikely to be “chatbots disappear.”

Instead, conversational AI and agentic AI will coexist.

---

The Model Becomes One Part of a Larger System

This is perhaps the most important conceptual change.

Traditional AI application:

Application
     ↓
AI model
     ↓
Answer

Agentic application:

                 AI application
                      │
          ┌───────────┼───────────┐
          ↓           ↓           ↓
        Model       Memory       Tools
          │           │           │
          └───────────┼───────────┘
                      ↓
                   Planner
                      ↓
                 Action loop
                      ↓
                  Environment

The AI model is still extremely important.

But the model alone is no longer the complete product.

The surrounding system determines what the AI can actually do.

---

Why AI Companies Are Competing to Build Better Agents

The shift toward agents also changes the competitive landscape.

The question is moving from:

Which company has the best chatbot?

toward questions such as:

• Which model reasons reliably?
• Which system can use tools effectively?
• Which agent can complete long-running tasks?
• Which system can use computers safely?
• Which platform has the best developer ecosystem?
• Which agent makes fewer mistakes?
• Which system can complete work at acceptable cost?

This is why companies such as OpenAI, Google and Anthropic are increasingly focusing on agentic capabilities alongside their core models.

The broader competition is no longer only about language generation.

It is about building AI systems capable of interacting with the digital world.

---

The Infrastructure Challenge Behind Agents

Agentic AI also creates a hardware challenge.

If one user request requires multiple model calls, large-scale deployment can create enormous inference demand.

More agents
     ↓
More actions
     ↓
More model calls
     ↓
More inference
     ↓
More accelerators
     ↓
More data-center capacity

This connects directly to the broader AI infrastructure story.

Agents may make inference efficiency even more important.

---

The Future of AI May Be Goal-Oriented

The transition can be summarized in three stages.

Stage 1: Ask

User
 ↓
Question
 ↓
AI
 ↓
Answer

Stage 2: Assist

User
 ↓
Task
 ↓
AI assistance
 ↓
Human completes work

Stage 3: Delegate

User
 ↓
Goal
 ↓
AI agent
 ↓
Plan
 ↓
Tools
 ↓
Actions
 ↓
Verification
 ↓
Completed work

The third stage is where the biggest change happens.

The user describes what they want, while the AI system determines many of the steps required to accomplish it.

---

AI Agents Are Moving Beyond Chatbots

AI agents represent a shift from conversation to execution.

Chatbots are primarily designed to respond.

Agents are designed to pursue goals through a sequence of actions.

That requires a combination of:

AI models
+
Reasoning
+
Planning
+
Tools
+
Memory
+
Software execution
+
Feedback
+
Permissions

The technology is still evolving, and reliable autonomous execution remains difficult.

But the direction is clear.

AI systems are increasingly being designed not simply to tell users how to perform a task, but to perform parts of the task themselves.

That changes the role of AI from an information interface into something closer to a software worker.

And if these systems become reliable enough, the biggest change may not be that people have better chatbots.

It may be that people increasingly interact with computers by giving them goals instead of instructions.