What Are AI Agents and How Do They Work?
AI Is Moving from Answers to Action
Not long ago, using AI often meant typing in a prompt, getting a response, and deciding what to do with it next. Today, AI systems are increasingly capable of taking action themselves: making decisions, using tools, and working through multiple steps to accomplish a goal.
That shift is where AI agents come in. Unlike traditional chatbots that respond to a single prompt, AI agents can plan and act across a workflow. They can gather information, use software and data, evaluate what they find, and determine what to do next.
They're also changing how software is built, how work gets done, and how teams approach complex problems, making it important for those in tech to be aware. Let’s break down what AI agents are, how they work, and why they’re becoming an important part of the technology landscape.
What Are AI Agents?
At a basic level, an AI agent is a software system that can perceive information, make decisions, use tools, and take actions to achieve a specific goal. Think of the difference between a chatbot and an agent this way:
- Chatbot: You give it a prompt → it generates a response.
- AI agent: You give it a goal → it creates a plan → uses tools → evaluates the results → takes the next action → repeats until the task is complete.
Imagine asking an AI agent to research your competitors. Instead of simply telling you what it knows, the agent could search for relevant information, gather and organize its findings, analyze what it discovers, create a summary, and identify trends or recommendations.
The level of autonomy can vary. Some agents may require people to approve each step, while others can complete more of the process independently. But the underlying idea is the same: AI agents are designed to move from generating information to accomplishing tasks.
How Do AI Agents Work?
So, what makes an AI agent different from a basic AI application? Several components work together to help an agent understand a goal and take action.
The Brain: Large Language Models
At the center of many AI agents is a large language model (LLM). The LLM helps the agent understand natural language, interpret context, follow instructions, and reason about what should happen next.
Think of it as the agent’s decision-making engine. If you give an agent a goal, the LLM helps break that goal into smaller steps and determine which action makes sense at each point in the workflow. But an LLM alone can’t necessarily complete the entire job...that’s where tools come in.
The Hands: Tools and APIs
An AI agent needs a way to interact with the world outside of its language model. That’s where tool calling comes in. Agents can be connected to tools such as:
- APIs
- Web searches
- Calculators
- Code interpreters
- Databases
- Business software and other applications
These tools give an agent the ability to do more than generate text; it can retrieve information, run calculations, execute code, update systems, or interact with other software. In other words, the LLM provides the reasoning, while tools give the agent a way to act on that reasoning.
Memory: Keeping Track of Context
Imagine trying to complete a complicated project while forgetting everything that happened five minutes ago. It wouldn’t get very far.
AI agents need memory, too. Short-term memory allows an agent to keep track of the current conversation, instructions, and actions within a workflow. Some systems can also use long-term memory, including technologies such as vector databases, to store and retrieve relevant information over time.
Memory helps agents maintain context, learn from previous interactions, and work through tasks that can’t be completed in a single step.
The Agentic Loop: Think, Act, Observe, Repeat
Here’s where things get particularly interesting. AI agents can operate through an agentic loop, or a continuous cycle that allows them to adjust their actions based on what happens next. The process might look something like this:
- Understand: Identify the goal.
- Plan: Determine the next step.
- Act: Use a tool or take an action.
- Observe: Review the result.
- Adjust: Reevaluate the plan based on what happened.
- Repeat: Continue until the goal is achieved.
This loop is a major part of what makes agentic systems different from one-and-done AI interactions. Instead of simply responding and stopping, the agent can use the outcome of one action to inform what it does next.
Common Types of AI Agents
Not every AI agent works the same way. Agents can range from relatively simple systems following predefined rules to more sophisticated systems that learn from experience.
Simple Reflex Agents
Simple reflex agents respond to specific conditions using predefined rules. They don’t maintain memory of previous interactions or make complex decisions.
An automated thermostat is a familiar example: when the temperature falls below a certain point, the system turns on the heat. Yes, it’s simple, but it demonstrates the basic idea of an agent responding to its environment.
Model-Based Agents
Model-based agents go a step further by maintaining an internal representation—or model—of their environment. This allows them to account for changes over time and make decisions based on more than what’s happening in the immediate moment.
For example, an agent navigating a changing environment can use its understanding of that environment to determine what action makes sense next.
Goal- and Utility-Based Agents
Goal-based agents are designed to work toward a specific objective. Rather than simply responding to a condition, they consider what actions could help them reach a desired outcome.
Utility-based agents take this a step further by evaluating different possible actions based on how desirable their outcomes are. Instead of asking, “What rule should I follow?” the system can ask, in effect, “Which option is most likely to produce the best result?”
Learning Agents
Learning agents improve their performance by using feedback and experience.
Rather than relying entirely on predefined instructions, they can adapt based on what they’ve learned. This ability to adjust over time is an important part of building increasingly sophisticated AI systems.
Why AI Agents Matter for Tech Professionals
AI agents must be on your radar because they have the potential to change not only the technology we build, but how we work with it.
AI agents can support workflows ranging from research and analysis to software development, customer support, data processing, and project management. They can automate repetitive tasks, connect different systems, and help teams tackle multi-step processes more efficiently.
For technology professionals, that creates new opportunities to rethink how work gets done. Instead of asking, “How can I use AI to do this task?” the question may increasingly become, “What workflow could AI help me rethink entirely?”
That doesn’t mean everyone needs to become an AI engineer. But understanding how AI agents work and where they can and can’t be trusted can help tech professionals make smarter decisions about emerging tools and identify new opportunities to innovate.
What Comes Next for AI Agents?
AI agents are still evolving, and their increasing capabilities come with important questions. How much autonomy should an AI system have? When should a person step in? How do we make sure an agent’s actions are accurate, secure, and aligned with its intended purpose?
As these systems become more capable, human oversight, reliability, security, and responsible AI practices will become increasingly important.
The goal is to build and use AI thoughtfully, understanding both its potential and its limitations. For anyone working in tech, staying curious and building AI fluency can help you keep pace as the field continues to change.
Take Your AI Knowledge Further at GHC
Reading about AI is one thing, but getting in the room with the people building what’s next is another.
At Grace Hopper Celebration 2026 in Anaheim, CA, you’ll have the opportunity to explore emerging technologies, hear from technology leaders and experts, and connect with professionals who are shaping the future of tech.
If you’re building AI systems, exploring how to bring AI into your organization, or simply trying to understand what’s coming next, GHC is a place to learn, exchange ideas, and discover new possibilities for your career. Register now!