What Are AI Agents? Examples and How They Work

Quick Definition

An AI agent is a software system that perceives its environment, decides what to do, and takes action to complete a goal, often across multiple steps, without needing a separate instruction for each step. Unlike a chatbot that waits for a prompt and responds once, an agent plans, acts, checks results, and continues until the task is done.

GitHub’s Copilot agent does not wait for a developer to tell it what to do next. A developer assigns it an issue, and it reads the codebase, writes code, runs tests, catches failures, fixes them, and opens a pull request for human review. That entire sequence happens without the developer typing another command.

That is what makes it an agent rather than an AI assistant. The distinction is not about how clever the underlying model is. It is about whether the system can plan a sequence of actions, execute them, observe what happened, and continue toward a goal.

This article explains what AI agents are, how they work mechanically, and where they are already being used at scale, with real examples rather than hypothetical ones.

What an AI Agent Actually Is

An AI agent is a software system that perceives inputs from its environment, reasons about what to do, takes actions using tools or interfaces, and repeats that cycle until a goal is achieved.

A standard AI model responds to a prompt. You give it input, it gives you output, done. An agent adds a loop: it uses its output to decide what to do next, acts on that decision, observes what happened, and continues. That loop is what separates an agent from a model.

The goal can be assigned in plain language (‘summarize these 50 contracts and flag any unusual clauses’) or structured as a task in a system (‘run this test suite and open a pull request’). What matters is that the agent pursues the goal across multiple steps rather than producing a single response.

 

The simplest way to think about the difference A chatbot answers. An AI agent acts. When you ask a chatbot to find flights, it searches and shows results. An AI agent can search, compare prices, check your calendar for conflicts, and book the best option.

How AI Agents Work: The Three Core Components

Every AI agent, regardless of complexity, operates around three functions: perception, reasoning, and action. Understanding these makes it easier to evaluate what any given agent can and cannot do.

Perception: What the Agent Can See

An agent needs inputs to work with. Depending on how it is built, it might perceive text (emails, documents, instructions), structured data (spreadsheets, database records), web content (search results, pages), code (repositories, test outputs), or other data streams.

The inputs define the agent’s field of view. An agent that can only read text cannot do anything useful with a spreadsheet unless something converts it first. Perception is the boundary of what the agent knows about at any given step.

Reasoning: What the Agent Decides

This is where the AI model sits. Given what the agent perceives, it decides what to do next. For a simple task, this might be a single decision. For a complex workflow, it involves planning a sequence of steps, deciding which tool to use at each step, and handling unexpected results when an action does not produce what was expected.

The quality of reasoning determines whether the agent stays on track, recovers from errors, and reaches the goal efficiently. This is why more capable underlying models tend to produce more reliable agents for complex tasks.

Action: What the Agent Does

Agents act through tools. A tool is any interface the agent can call: a search engine, a database, an API, a code execution environment, a file system, a calendar, an email client. When the agent decides that the next step is to search for something, it calls the search tool. When it needs to write to a file, it calls the file tool.

The range of available tools is the range of possible actions. An agent without a search tool cannot search. An agent without file access cannot write files. Tool design is where most of the practical work in building agents happens.

 

Component	What It Does	Real Example
Perception	Takes in data from the environment	Reads an assigned GitHub issue and the relevant codebase files
Reasoning	Decides what step to take next	Determines which files to edit and what tests to run first
Action	Executes the decision using a tool	Edits the files, runs the test suite, reads the failure output
Observation	Reads the result of the action	Test passed or failed, which determines the next step
Loop	Repeats until goal is reached	Continues until tests pass and a pull request is ready

 

If you want to read more about how content structure affects AI systems that use these components to retrieve and cite information, read this article on LLM SEO.

-> Recommended reading: LLM SEO: Optimizing Content for Large Language Models

Real-World AI Agent Examples

The clearest way to understand what agents actually do is to look at deployments that are already running in production, with named organizations and documented outcomes.

GitHub Copilot Agent: Coding

GitHub launched its Copilot coding agent in May 2025. When a developer assigns a GitHub issue to Copilot, the agent opens a secure development environment, clones the repository, reads the codebase, writes the code needed to address the issue, runs the test suite, reads the failure output, fixes problems, and opens a draft pull request. The developer reviews and approves.

The agent operates inside the same branch protection and required-review gates as a human engineer. It does not merge code. A human still makes the final decision. The value is in eliminating the time between ‘issue assigned’ and ‘pull request ready for review.’

Shopify Sidekick: Business Operations

Shopify’s Sidekick is embedded in the merchant admin interface and lets store owners run multi-step business operations in plain language. A merchant can ask it to ‘show me my best month this year and write a campaign brief targeting those buyers.’ Sidekick identifies the month from sales data, segments the relevant customers, and drafts the brief without the merchant opening a separate analytics tool or writing tool.

According to Shopify’s engineering blog, Sidekick has evolved from a simple tool-calling system into a full agentic platform that handles analyzing customer segments, filling product forms, writing SEO descriptions, and navigating complex admin interfaces, all operations that previously required clicking through multiple screens or writing custom queries.

JPMorgan LLM Suite: Internal Workflows

JPMorganChase deployed LLM Suite, an internal platform giving employees secure access to large language models for drafting, analysis, and eventually agents that execute multi-step workflows against the bank’s internal data. The bank reported going from zero to 200,000 onboarded users within eight months, driven by employee demand. Agents operate inside the bank’s security boundary with approved data scopes and audit trails on every API call.

DHL HappyRobot: Logistics Coordination

DHL Supply Chain deployed voice and email AI agents built by HappyRobot to handle appointment scheduling, driver follow-up calls, and warehouse coordination. According to DHL’s November 2025 press release, the agents autonomously handle phone and email interactions, enabling faster, more consistent, and scalable communication across operations. The agents work in the voice-first communication layer that traditional logistics software cannot reach: scheduling, confirming, and following up on the kinds of conversations that previously happened by phone and never entered the transport management system.

Khan Academy Khanmigo: Education

Khan Academy deployed Khanmigo as an AI tutor and teacher assistant across its global platform. Khanmigo does not simply answer student questions. It guides students through reasoning, asks follow-up questions, adjusts difficulty based on responses, and flags students who may need teacher intervention. In the 2024-2025 academic year, Khanmigo reached a 731% increase in reach year over year across students, teachers, and parents.

Genentech Research Workflows: Life Sciences

Genentech built agent ecosystems on AWS to automate complex research workflows, enabling scientists to focus on drug discovery rather than data management. Multiple specialized agents coordinate across research processes: one handles data retrieval, another runs analysis, another formats outputs for review. The coordination layer removes the manual handoffs that previously slowed research pipelines.

Types of AI Agents

Not every agent works the same way. Three categories cover most of what is in production today.

Single-Task Agents

A single-task agent is designed to complete one specific job reliably. It has a narrow set of tools, a clearly defined goal, and a short action loop. A customer support agent that reads incoming support tickets, looks up relevant documentation, and drafts a reply is a single-task agent. It does not try to do anything beyond that workflow.

Single-task agents are the most reliable in production. The narrower the task, the easier it is to test, monitor, and fix when something goes wrong.

Multi-Step Agentic Workflows

A multi-step workflow agent executes a longer sequence of actions to reach a more complex goal. The GitHub Copilot example above is a multi-step workflow: reading an issue, understanding the codebase, planning edits, writing code, running tests, and fixing failures is not a single action but a sequence of related decisions and actions.

Multi-step agents require more robust error handling. If step three fails unexpectedly, the agent needs to either recover and continue or stop and report the problem clearly.

Multi-Agent Systems

A multi-agent system uses more than one agent working in coordination. Each agent specializes in a specific function, and a coordinating layer routes work between them. Genentech’s research workflow is a multi-agent system: one agent handles retrieval, another handles analysis, another handles output formatting.

Multi-agent systems are well suited to complex, interdisciplinary problems where specialization improves quality. The tradeoff is increased orchestration complexity. More agents means more coordination logic, more potential failure points, and more difficulty debugging when something goes wrong.

 

Type	Best For	Example
Single-task agent	Narrow, repeatable workflows with clear inputs and outputs	Support ticket routing and draft reply generation
Multi-step workflow agent	Complex tasks requiring sequential decisions over many steps	GitHub Copilot: issue to pull request
Multi-agent system	Problems requiring specialized expertise across multiple domains	Genentech research pipeline with coordinated specialist agents

 

What AI Agents Can and Cannot Do Reliably

The range of capabilities in production agents varies considerably. Being precise about current limitations is more useful than a general endorsement or dismissal.

What Agents Do Well

•         Structured, repeatable workflows where the inputs are consistent and the success criteria are clear.
•         Tasks that combine multiple existing tools: search, read, write, calculate, send.
•         Long-horizon tasks where the bottleneck was human attention rather than expertise.
•         Processing large volumes of documents, records, or data that would take a human team significant time.
•         Coordination tasks where the work is mostly routing, summarizing, and formatting between systems.

Where Agents Still Struggle

•         Tasks requiring genuine judgment or contextual common sense that was not encoded in training data.
•         Open-ended creative or strategic work where the goal itself is unclear.
•         Anything requiring real-time situational awareness outside of the agent’s tool set.
•         Long action sequences where early errors compound before they are caught.
•         Tasks where the cost of a mistake is high and recovery is difficult without human intervention.

The most reliable agentic deployments in production share a pattern: narrow scope, clear success criteria, human review at decision points, and audit logs for every action. Agents that work autonomously across broad, ambiguous tasks without human checkpoints are where most documented failures occur.

Who Should Be Thinking About AI Agents Right Now

Founders

If your business has a repetitive workflow that currently requires a person to gather information from multiple sources and produce a structured output, that workflow is a reasonable starting point for an agent. Customer onboarding, invoice processing, contract review, and lead qualification are common early deployments. The key is starting narrow and measuring the agent’s accuracy before expanding scope.

Developers

The infrastructure for building agents has matured significantly. Tools like LangGraph, OpenAI’s Agents SDK, and Anthropic’s tool-use API make it practical to build production-grade agents without starting from scratch. The most important engineering decision is not model selection but tool design and error handling. Most agent failures in production are architecture failures, not model failures.

Marketers

Agents are beginning to affect how content is discovered through AI search engines. When someone asks ChatGPT or Perplexity a question, an agent retrieves and synthesizes relevant content from the web. How your content is structured affects whether it gets retrieved and cited. If you want to read more about how to get your content cited by AI tools, read this article on how to rank in ChatGPT.

-> Recommended reading: How to Rank in ChatGPT: A Step-by-Step Citation Strategy

Key Takeaways

•         An AI agent perceives its environment, reasons about what to do, takes action using tools, observes the result, and repeats that loop until a goal is achieved.
•         The difference between an AI assistant and an agent is the loop: an assistant responds to one prompt; an agent plans and executes a sequence of steps.
•         Agents have three core components: perception (what they can see), reasoning (what they decide), and action (what they do using tools).
•         Real production examples include GitHub Copilot (coding), Shopify Sidekick (business operations), JPMorgan LLM Suite (internal workflows), DHL HappyRobot (logistics), Khan Academy Khanmigo (education), and Genentech research pipelines (life sciences).
•         Three categories of agents cover most production deployments: single-task agents, multi-step workflow agents, and multi-agent systems.
•         Agents work best on narrow, structured, repeatable tasks with clear success criteria and human review at key decision points.
•         Gartner projects 40% of enterprise applications will include task-specific AI agents by end of 2026, up from less than 5% in 2025.
•         Most agent failures in production are architecture and tool-design failures, not model failures.

FAQ

What is an AI agent in simple terms?

An AI agent is a software system that can complete a goal across multiple steps by deciding what to do next, taking action, and adjusting based on what happens. Unlike a chatbot that responds to one prompt at a time, an agent keeps working until the task is done.

What is the difference between an AI agent and a chatbot?

A chatbot waits for a prompt and produces a response. An agent plans a sequence of steps, takes actions using tools (search, databases, code execution, APIs), reads the results, and continues until the goal is achieved. A chatbot answers. An agent acts.

What are some real examples of AI agents?

GitHub Copilot agent (turns a code issue into a pull request), Shopify Sidekick (runs multi-step business operations in plain language), Khan Academy Khanmigo (guides students through learning sequences), DHL’s logistics agents (handles scheduling and coordination by voice and email), and JPMorgan’s LLM Suite (executes multi-step internal workflows against bank data) are all verifiable production deployments.

How is agentic AI different from regular AI?

Regular AI models respond to a single prompt and produce a single output. Agentic AI adds a planning and action loop: the model decides what to do, does it using tools, reads the result, and continues. Agentic AI is goal-directed and multi-step rather than prompt-directed and single-output.

Are AI agents safe to use in business workflows?

For structured, narrow workflows with clear success criteria and human review at key decision points, agents are increasingly reliable in production. The risk increases with scope: agents given broad autonomy over open-ended tasks without checkpoints are where most documented failures occur. The safest deployments start narrow, measure accuracy, and expand scope only after establishing reliable performance.

Do AI agents replace human workers?

In current production deployments, agents more often remove bottlenecks from existing workflows than replace entire roles. GitHub Copilot does not replace developers; it eliminates the time between issue assignment and pull request. DHL’s voice agents handle coordination calls that previously created scheduling delays, freeing staff for work that requires judgment. The pattern across most documented deployments is augmentation of specific workflow steps, not wholesale replacement.

What tools do AI agents use?

Tools are any interface an agent can call to take action: search engines, databases, APIs, code execution environments, file systems, calendars, email clients, and external services. The range of tools available to an agent defines what it can and cannot do. Most agent platforms allow developers to define custom tools that connect to their own systems and data.

Conclusion

An AI agent is not a more powerful chatbot. It is a different kind of system: one that plans, acts, observes, and continues rather than waiting for the next prompt.

The examples that make this real are all in production. GitHub assigns issues to an agent and gets pull requests back. Shopify merchants run multi-step business operations in plain language. JPMorgan’s 200,000 users access agents that work against internal bank data. The concept is no longer theoretical.

The practical starting point for anyone evaluating agents is the same regardless of role: find a workflow that currently requires a person to gather information from multiple places and produce a structured output, start narrow, define clear success criteria, keep a human in the review loop, and measure accuracy before expanding. That pattern is what separates the deployments that work from the ones that get stuck at pilot.

References

•         GitHub Blog: Introducing the GitHub Copilot Coding Agent (May 2025)
•         Shopify Engineering Blog: How Sidekick Became an Agentic Platform (August 2025)
•         JPMorganChase Technology Blog: LLM Suite – Zero to 200,000 Users (June 2025)
•         DHL Press Release: HappyRobot AI Agent Deployment (November 2025)
•         Khan Academy: Khanmigo Annual Impact Report 2024-2025
•         Gartner Q1 2026: AI Agent Enterprise Adoption Forecast
•         IDC: AI Copilot Integration Forecast 2026

About the Author

I’m Sanwal Zia, an SEO strategist with more than six years of experience helping businesses grow through smart and practical search strategies. I created Optimize With Sanwal to share honest insights, tool breakdowns, and real guidance for anyone looking to improve their digital presence. You can connect with me on YouTube, LinkedIn, Facebook, Instagram, or visit my website to explore more of my work. 

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