AI Agents Explained: How They Work, What They Can Do, and What Comes Next

In February 2024, Klarna turned on an AI agent to handle customer support. Within 30 days, it had resolved 2.3 million conversations on its own which is work equivalent to roughly 700 full-time employees.

But JPMorgan tells a different version of the same story. Roughly 250,000 employees now have access to the bank's internal AI platform, LLM Suite, with about half using it daily. The bank has now begun moving beyond single-response assistance into agentic systems that handle multistep tasks across business lines. 

It's a useful contrast to Klarna where one company automated a narrow, high-volume task end to end, while the other is embedding agents gradually across a much broader, more complex workforce.

Numbers like these make AI agents difficult to ignore. They're becoming part of everyday business, healthcare, education, and software, even as companies continue testing where the technology actually holds up. 

So, the gap between AI's promise and what businesses are actually achieving remains much wider than many headlines suggest.

That's exactly why I have put together this AI Agents Explained guide. Keep reading and you'll learn how AI agents work, where they already deliver results, where they still fall short, and why so many companies are still trying to separate genuine progress from marketing hype.

In my guide, I have covered: 

1. What Is an AI Agent
2. AI Agents vs. Chatbots vs. Assistants
3. How AI Agents Actually Work
4. The Different Types of AI Agents
5. What AI Agents Can Actually Do Right Now
6. Where AI Agents Still Struggle
7. Where the Hype Outpaces the Reality
8. What Comes Next for AI Agents &
9. How to Spot a Real Agent vs. Marketing

What Is an AI Agent?

An AI agent is a software system that receives a goal, decides how to achieve it, carries out the required actions, and changes its approach whenever something doesn't go as planned. Unlike a traditional chatbot that mainly responds to prompts, an AI agent can use external tools, work through multiple steps, and continue toward its objective with little or no ongoing human input.

McKinsey's State of AI report, published in November 2025, found that only 23% of organizations had scaled AI agents into at least one business function. Another 39% were still experimenting, and in no business area had more than about 10% of organizations fully rolled agents out at scale. 

IBM's separate CEO study paints a similar picture. Only 25% of AI initiatives delivered the return on investment executives expected, suggesting that successful deployment remains much harder than the growing hype around AI might imply.

The concept isn't new. AI researchers have studied intelligent agents for decades. The real breakthrough came with large language models, which gave these systems the reasoning ability to plan, make decisions, and adapt as they work.

A good example comes from Amazon's warehouses. Its robots receive a task, identify the fastest route, avoid obstacles, and adjust their movements as inventory changes around them. They don't follow a fixed path. They make decisions in real time to complete the job as efficiently as possible. 

Another technical example could be Cognition Labs' Devin, an AI software engineering agent. Give it a GitHub bug report, and it plans a solution, opens the relevant files, writes code, runs tests, and revises its work whenever something fails. 

That repeated cycle of planning, acting, checking, and adjusting is what separates an AI agent from a standard chatbot. In a vendor-reported benchmark disclosed by Cognition Labs, Devin resolved 13.86% of issues unassisted on a 25% subset of SWE-bench, a benchmark built from real GitHub issues, compared with a previous best result of 1.96% at the time. The figure reflects Devin's early performance as reported by its own developer, not an independently verified or ongoing capability score. 

AI Agents vs. Chatbots vs. Assistants

Search AI agents today, and the lines become blurry almost immediately. Customer support chatbots, AI assistants, workflow automations, and even prompt templates all end up carrying the same label. That's why so many people struggle to understand what an AI agent actually is and how it differs from the tools they already use.

But the difference is much simpler than the marketing suggests. 

• A chatbot mainly responds to questions. 
• An AI assistant helps you complete tasks. 
• A true AI agent can decide what needs to happen next, use different tools, and continue working toward a goal without someone directing every step.

Bot

AI Assistant

AI Agent

Follow a script?

Yes, strictly

Loosely, guided by you

No, sets its own steps

Need your input at each step?

Constantly

Often

Rarely, only for approval

Can use outside tools on its own?

No

Sometimes, if built in

Yes, that's the core idea

Remember earlier steps in a task?

No

A little

Yes, tracks progress actively

Real-world example

A website FAQ popup

Siri answering one question

ChatGPT Agents

IBM explains the difference in a simple way. A chatbot responds. An AI agent gets the job done. A chatbot answers a question and waits for the next prompt. An AI agent connects to external tools, retrieves information, calls APIs, decides what needs to happen next, and completes the task.

Klarna's AI customer support agent shows the difference clearly. Instead of telling customers how to request a refund or check an order, it verifies account details, processes refunds, and resolves the request on its own. That's the point where an AI agent stops acting like a chatbot and starts acting like a digital worker.

How AI Agents Actually Work

Every genuine AI agent follows the same four-part loop beneath the marketing language. Miss one of these building blocks, and you've built something much closer to a chatbot or an AI assistant, regardless of what the product page claims. This loop is what separates AI agents from software that simply responds to prompts.

1. Planning

Planning is where an AI agent decides how to reach a goal before taking action. Instead of waiting for you to spell out every instruction, it breaks a request into smaller tasks, chooses the right order, and builds a plan that can change if new information appears.

An example is Waymo's self-driving vehicles. Before moving an inch, the system builds a driving plan using maps, traffic conditions, nearby vehicles, pedestrians, and road rules. That plan keeps changing every second as new information arrives, but the goal never changes, reaching the destination safely. 

2. Tool Use

Tool use is the feature that turns an AI agent into something useful outside a conversation. Without access to external systems, an AI can only generate text. Once it connects to APIs, databases, browsers, calendars, payment systems, or company software, it can complete real work instead of describing how someone else should do it.

GitHub Copilot's coding agent provides a good example. It can open project files, edit multiple documents, run build commands, execute tests, inspect the results, and submit changes for review. Every one of those actions happens outside the conversation itself. 

3. Memory

Memory allows an AI agent to remember what has already happened during a task. It keeps track of previous actions, stores important information, avoids repeating mistakes, and uses earlier results to decide what should happen next. Without memory, every response becomes a fresh conversation.

Amazon's warehouse robots rely on the same principle. They remember shelf locations, completed deliveries, traffic around nearby robots, and updated warehouse layouts as they continue working. Without memory, every movement would become a completely new decision. 

4. Adaptation

Real-world work rarely follows a perfect plan. APIs fail, websites change, customers provide incomplete information, and software behaves unexpectedly. A chatbot usually reports the problem and waits for another prompt. An AI agent evaluates what went wrong, selects another approach, and keeps working toward the original goal. If it genuinely can't solve the problem, a well-designed agent escalates it to a human instead of leaving the task unfinished.

Sierra, the AI agent platform used by companies like SiriusXM and Sonos, offers a good example. When a customer asks something the agent can't confidently resolve, perhaps a billing dispute requiring account access or a request that falls into a policy grey area, it doesn't invent an answer or leave the customer waiting. 

It recognises its limits and transfers the conversation to a live representative, carrying over the relevant context so the customer doesn't have to start again. The same pattern appears across many AI agents. If an API stops responding, data is missing, or a website changes unexpectedly, the agent will usually try another approach before deciding the task needs human intervention.  

The Different Types of AI Agents

Not every AI agent works the same way. Some follow simple rules. Others analyse information, compare different options, learn from experience, and improve with every task they complete. Understanding these different types of AI agents makes it much easier to see why one system can only perform a single repetitive task while another can handle complex problems with very little human input.

1. Simple Reflex Agents

Simple reflex agents react only to what is happening right now. They don't remember previous events or think about what might happen next. Every action follows a fixed rule triggered by a specific input.

A household thermostat is the classic example. The temperature drops below a preset value, the heating switches on. The temperature rises high enough, the heating switches off. The same principle also appears in automatic doors and motion-sensor lights.

2. Model-Based Reflex Agents

Model-based reflex agents keep an internal representation of their surroundings. That extra context helps them make better decisions when the current input doesn't tell the whole story.

Robot vacuum cleaners such as Roomba demonstrate this well. Instead of cleaning randomly every time, newer models build a map of the home, remember obstacles, recognise completed rooms, and choose more efficient routes during future cleaning cycles.

3. Goal-Based Agents

Goal-based agents begin with a clear objective, then work backward to decide the best sequence of actions for reaching it. They compare different possibilities before choosing the one most likely to succeed.

Navigation systems provide an everyday example. Google Maps doesn't simply follow the first available road. It evaluates multiple routes, traffic conditions, road closures, and estimated travel times before recommending the fastest or most practical option.

4. Utility-Based Agents

Utility-based agents don't stop after finding a solution. They compare several successful options and choose the one that produces the best overall outcome by balancing speed, cost, safety, efficiency, or other competing priorities.

Airline pricing systems work in a similar way. Instead of filling every seat at the same price, they constantly adjust fares by weighing demand, remaining seats, booking history, and expected future sales to maximize revenue.

5. Learning Agents

Learning agents become more effective as they receive feedback. They analyse previous decisions, identify mistakes, and improve future performance without engineers rewriting every rule manually.

Recommendation systems on platforms like Netflix and Spotify follow this approach. The more people watch, skip, like, or search, the better these systems become at predicting what users want next.

6. Modern LLM-Based AI Agents

When people talk about AI agents today, they're usually referring to LLM-based agents. These systems combine several of the categories above into a single architecture. They can plan tasks, remember previous actions, use external tools, evaluate different options, and adapt whenever something unexpected happens.

ChatGPT Agent is a good example. Give it a complex objective, and it can break the task into smaller steps, browse the web, gather information, interact with software, adjust its plan when new information appears, and continue working until the objective is complete. That's why modern LLM-based AI agents look very different from traditional chatbots or rule-based automation systems.

What AI Agents Can Actually Do Right Now

AI Agents in Software Development

Software development remains one of the fastest-moving AI agent use cases. AI coding agents can review repositories, plan changes, write code, run automated tests, fix errors, and repeat that cycle until the task is complete. Companies using tools like GitHub Copilot's coding agent and Claude Code already report faster development workflows, while benchmark performance continues improving.

AI Agents in Customer Support and Business Operations

Customer support has become another major success story for AI agents in business. Instead of answering straightforward questions, today's agents retrieve customer records, verify account information, process refunds, update orders, and resolve requests without handing every conversation to a human representative. Klarna's deployment demonstrated how quickly this technology can scale after completing work equivalent to roughly 700 full-time employees during its first month.

The same pattern now appears in other business functions:

• Banking: Fraud-detection agents monitor transactions continuously and can block suspicious payments within seconds.
• Insurance: Claims-processing agents compare uploaded documents with policy rules, identify missing information, and decide which claims can move forward automatically.
• E-commerce: Recommendation agents analyse browsing history, purchases, and customer behaviour to personalise product suggestions in real time.

AI Agent Adoption by Industry

Even with these successes, AI agent adoption varies considerably between industries. Banking and insurance have moved fastest, largely because both industries already rely on structured digital data and highly repetitive workflows, conditions that make automation much easier to implement safely.

Healthcare and government face a different reality. Privacy regulations are stricter, mistakes carry much higher consequences, and many organizations still depend on legacy systems that weren't designed for autonomous software. Those factors slow deployment even when the underlying technology is ready.

Where AI Agents Still Struggle

AI agents have improved remarkably, but they still fail in predictable ways. That's the side of the technology many product demos skip. Knowing those weaknesses makes it much easier to judge when an AI agent is the right tool and when a human should stay in the loop. 

1. Unpredictable physical environments:

Agents operating purely in software have a huge advantage over ones that need to act in the physical world. Tasks needing real-time adaptation and complex motor skills, surgery, certain construction work, disaster response, remain far outside what today's agents handle safely alone.

2. High cost and heavy resource needs:

Building and running a sophisticated agent takes real computing power, and that cost adds up fast at scale. Devin's pricing dropped from $500 a month at launch to $20 a month within about a year, as the underlying infrastructure got more efficient, a pattern likely to repeat industry-wide.

3. Compounding errors in long tasks:

Because an agent chains many steps together, a wrong assumption early on can carry through every step after it, unlike a single chatbot reply that's simply right or wrong on its own. This is part of why Devin's real-world success rate on complex tasks (14-15%) trails its performance on narrower benchmark tasks.

4. Trust and oversight gaps:

IBM's own research puts it plainly - even though agents can technically operate without supervision, the most responsible deployments still include human oversight to catch mistakes early. Klarna's decision to rebuild human escalation paths after its 2025 quality issues is a direct, public example of that lesson being learned in production.

Where the Hype Outpaces the Reality

AI agents are appearing in products at an incredible pace, but successful deployment is moving much more slowly. That gap becomes obvious once you look past the product headlines and check what's actually running inside companies. 

Research from S&P Global Market Intelligence and McKinsey suggests only 31% of enterprises have AI agents performing meaningful work in production, and IBM's CEO study tells a similar story, in which only 25% of AI initiatives met the business results leadership expected. 

The biggest challenge isn't building an AI agent anymore. It's finding work that actually benefits from one. Many companies discover that autonomous software performs well on structured, repetitive tasks but struggles once human judgement, changing priorities, or incomplete information enter the picture. 

That's why most successful deployments still keep people involved, reviewing decisions, handling exceptions, and taking over whenever confidence drops. The strongest results usually come from combining AI agents with experienced employees rather than expecting one to replace the other completely.

What Comes Next for AI Agents

The direction of travel is becoming much easier to predict. Gartner expects roughly 40% of enterprise applications to include a task-specific AI agent by the end of 2026, compared with less than 5% at the beginning of 2025. 

That growth isn't happening because AI models suddenly became dramatically smarter. Much of it comes from better infrastructure. Standards like the Model Context Protocol (MCP) are making it easier for AI agents to connect with databases, business software, APIs, and other digital tools through a common framework. The number of publicly available MCP servers has already passed 9,400, showing how quickly that ecosystem is expanding.

The bigger question is no longer whether AI agents become part of everyday software. That shift is already underway. The real challenge is deciding where they should work independently and where people should stay involved. Companies that define those boundaries early are likely to see the strongest results. Those that expect autonomous systems to handle every decision on their own will probably spend the next few years discovering exactly where the limits still are.

How to Spot a Real AI Agent

Almost every new AI product claims to be an AI agent now. In many cases, it's simply a chatbot or an AI assistant with a new label. These three questions will help you tell the difference.

1. Can it complete several steps without waiting for another prompt?

A real AI agent doesn't stop after producing one response. It breaks a goal into smaller tasks and continues working until the objective is complete. If every new step depends on another instruction from you, you're probably using an AI assistant rather than an AI agent.

2. Can it interact with external tools and systems?

This is one of the clearest signs of a genuine AI agent. It should be able to search databases, call APIs, update records, browse the web, send emails, schedule meetings, or interact with business software. If the tool only generates text explaining what you should do next, it isn't operating as an AI agent.

3. What happens when something goes wrong?

Real-world work rarely goes exactly as planned. A genuine AI agent detects failures, adjusts its approach, and tries another solution before giving up. Simpler systems usually stop at the first error and wait for another prompt. That ability to recover, adapt, and continue working is one of the biggest differences between an AI agent and a traditional chatbot.

A Quick Glossary for Beginners

Term

What it actually means

LLM (Large Language Model)

The underlying AI model, like GPT or Claude, generates language and reasoning. It's the "brain," not the whole agent.

Agentic AI

Another name for the same idea as AI agents, systems that act toward a goal rather than just responding to one prompt.

Orchestration

The layer that coordinates multiple steps, or multiple agents, working on a bigger task together.

Tool calling

The technical term for an agent reaching outside itself to use an API, run code, or access another system.

MCP (Model Context Protocol)

A shared standard letting AI tools and agents connect to outside systems consistently, instead of custom connections for every use case.

Hallucination

When an AI confidently states something false, a real risk for agents since a wrong early assumption can compound through every later step.

Final Words

AI agents are no longer a concept you'll only find in research papers or product launches. They're already writing code, reviewing claims, detecting fraud, handling customer support, and quietly taking over repetitive work inside thousands of businesses. At the same time, they're nowhere near replacing human judgement in every situation.

The biggest takeaway from this AI Agents Explained guide is simple. Don't judge an AI agent by the marketing around it. Judge it by what it can actually do on its own, how reliably it reaches its goal, and where human oversight still matters. The technology will keep improving, but understanding those differences today will put you well ahead of most people trying to keep up with AI.

FAQs

Is an AI agent just a chatbot?

No. A chatbot responds to prompts and waits for the next one. An AI agent can plan multiple steps, use external tools, adapt when something goes wrong, and continue working toward a goal with minimal human input.

Do AI agents work reliably today?

Yes, but mainly for structured tasks. AI agents perform well in areas such as coding, customer support, fraud detection, and document processing. More open-ended or high-risk work still requires human oversight.

What's the difference between an AI agent, an AI assistant, and a chatbot?

The biggest difference is autonomy. A chatbot answers questions. An AI assistant helps you complete tasks with regular guidance. An AI agent decides what to do next, uses tools, and continues working until the objective is reached.

What are some real examples of AI agents?

AI agents are already used in software development, customer support, banking, insurance, cybersecurity, and e-commerce. They review code, detect fraud, process claims, personalise recommendations, and automate repetitive business workflows.

Will AI agents replace human jobs?

Not entirely. AI agents are better suited to repetitive, rules-based work than tasks requiring judgement, creativity or relationship-building. Most organisations are using them to automate routine work while employees focus on more complex decisions.