What is an AI agent?

If you're like me, when you hear "agent," you think of secret agents doing cool stuff. But they mean a whole different thing in the world of AI. AI agents can be secret agents, but they can also be your voice assistant, your game opponents, or the system routing your support tickets.

This guide covers what AI agents actually are, how they work under the hood, and what you need to know to build your own AI agent systems.

What is an AI agent?

An AI agent is software that can perceive its environment, make decisions, and take actions to achieve specific goals without constant human guidance.

In the constantly changing and evolving landscape of artificial intelligence, “agents” are a cornerstone concept that represent things that do things. Sounds super vague, right? Because it intentionally is.

Agents can be simple and reflexive, meaning when you drive through a red light, the red light camera snaps a picture of your license plate. Agents can also consistently monitor levels of internet traffic over time and use that historic data to determine when an alert needs to be sent because traffic isn’t lining up with what it “should be.”

For example, did you know that your computer opponents in Mario Kart aren’t all trying to come in first? The computer-controlled racers are assigned a rank that they are “supposed” to come in at the end of the race. Each agent perceives where they are in the race, how fast they are going, how much they can accelerate, etc., and takes actions like speeding up, slowing down, using items, etc., to reach their goal of finishing the race in their assigned rank.

Nowadays, though, AI agents can move far beyond game AI. They're booking your meetings, writing code, processing insurance claims, and doing a million other things we’d never have thought a bot could do.

How AI agents work

AI agents work by observing their environment, interpreting what those observations mean, deciding on an action, taking that action, and updating their understanding based on the results. This means that over time, they learn which choices lead to better outcomes.

While modern AI agents (think ChatGPT) seem to do things near-instantly, peeking under the hood will show you that they actually follow a six-step processing cycle to turn user requests into autonomous action:

1. Perception: The agent receives input like text prompts, voice commands, API calls, or multimodal data like images and audio. This raw data becomes the foundation for everything that comes next.
2. Planning: The agent breaks down complex goals into a series of steps. If you ask it to "analyze our Q3 sales performance," its plan might look something like: query the database, aggregate by region, calculate trends, generate visualizations, write summary.
3. Retrieval: The agent searches its knowledge bases for any information that’ll help it do its job. This is where Retrieval Augmented Generation helps it ground its responses in facts instead of making things up.
4. Tool execution: The agent starts to act, calling APIs, running code, or triggering workflows.
5. Reasoning: After each action, the agent evaluates the results and decides what to do next. Did the database query return what we need? Should we try a different approach? This feedback loop is what makes agents truly autonomous.
6. Response: Finally, the agent generates an output and updates its agent memory with what it learned for future interactions.

Agents have two fundamental components:

• Sensors to perceive their environment.These range from simple data inputs (e.g., a camera at an intersection) to multimodal inputs (e.g., images and audio and complex natural language processing).
• Actuators to interact with their environment. These can be anything from a camera snapping a photo to a conversational AI producing natural language responses or an autonomous system adjusting the settings on a machine.

When we move to something like the racers in Mario Kart, we add reasoning and a goal. Each NPC has a goal position in the lineup that they’re trying to achieve. Their perception involves their current place in the lineup, their speed, whether or not they can accelerate more, whether or not they have items to use, etc.

The racer then has to figure out what the best course of action is to reach their goal position. Is it to use an item and then accelerate? Is it to accelerate and save the item for later?

These racers operate using a model trained through practice races and historical experiences to make that decision. But because all of these models are based on probabilities, the racer may make different decisions in different races, producing an engaging experience for the player, no matter how many times they play.

AI agents vs. AI assistants vs. chatbots

I’ve seen people use “AI agents,” “AI assistants,” and “chatbots” interchangeably, but they describe fundamentally different systems. Hint: the key difference is autonomy and complexity.

Here’s a quick cheatsheet:

AI agents take goals and execute them autonomously. Tell an agent to "research our competitors' pricing and update our strategy doc," and it searches the web, analyzes data, makes comparisons, and writes recommendations without you micromanaging each step.

AI assistants like ChatGPT or Claude respond to your prompts but wait for you to make decisions. They're reactive. You ask, they answer. Useful, but limited to the conversation context.

Chatbots follow decision trees or simple pattern matching. They handle FAQs and basic support tickets, but can't adapt to unexpected scenarios or learn from interactions.

And here’s a table comparing each term based on their different features:

Types of AI agents

AI agents range from simple to very complex. Here's the taxonomy:

• Simple reflex agents make decisions based solely on their current perception, without considering the history of events. They are reactive agents and lack the ability to plan for the future.
• Model-based reflex agents maintain a model of the world and are able to act in a partially observable environment because of it. These agents have knowledge of how their actions affect their world.
• Goal-based agents have been given a specific goal and then evaluate different actions based on how well they contribute to achieving those goals. These agents have some amount of choice.
• Utility-based agents assign value and utility to different outcomes and choose actions that maximize that expected utility. This introduces a consideration of preferences and trade-offs.
• Learning agents incorporate feedback, typically via machine learning, to adapt and improve their behavior over time based on experience. This is a key aspect of generative AI, where the system learns to generate new content based on its training data.

Once you have an understanding of each agent, you can start putting them together into multi-agent systems.

These systems can be homogeneous (all agents have the same capabilities) or heterogeneous (agents have different specializations). They can be cooperative, working together toward a shared goal, or competitive, each pursuing their own objective.

You can also build hierarchical systems where high-level agents set goals and low-level agents handle the grunt work. This lets both types focus on what they do best.

Applications of AI agents

AI agents have long moved out of their research labs and into systems across every industry. Here are some of the applications I’ve seen gain a lot of traction:

Customer-facing automation is probably where you've seen agents most. Customer support AI handles support tickets end-to-end, understanding problems, searching knowledge bases, executing fixes, escalating to humans when needed. Virtual assistants like Siri and Alexa have evolved from simple command execution to multi-step task completion. And in sales, agents research prospects, personalize outreach, schedule meetings, and update CRMs autonomously based on user behavior signals.

Enterprise operations also often run on agents now. They process invoices, reconcile accounts, generate reports, and handle compliance checks across multiple systems without human intervention. On the technical side, AI coding agents like GitHub Copilot and Cursor write features, debug issues, and review pull requests. They’re essentially acting as pair programmers that understand your entire codebase.

High-stakes domains use AI agents for important decisions at scale. In financial security, fraud detection agents flag suspicious transactions in real-time by spotting patterns that deviate from normal behavior. Healthcare takes a different approach, with agents providing treatment recommendations based on patient data and medical literature, then monitoring vitals and alerting providers before small issues become emergencies.

Physical systems use agents to interact with the real world. Robotics agents range from assembly line controllers to autonomous vehicles, perceiving traffic patterns and object dimensions to coordinate physical actions. On a larger scale, environmental monitoring agents track weather patterns and crop yields, autonomously adjusting irrigation or energy distribution based on conditions.

Gaming might seem trivial, but a lot of agentic innovation happens here. Remember our Mario Kart racers? The Director in Left 4 Dead does something similar. It adjusts enemy intensity based on how well you're playing, keeping the game challenging but not frustrating.

Challenges & limitations

With a lot of AI systems, hallucinations are the biggest issue. You can mitigate this through RAG grounding, semantic caching, and human-in-the-loop validation, but you can't eliminate it.

You’d also need to watch out for ethical and governance challenges like bias amplification, transparency in multi-step reasoning, and accountability. Who’s on the hook when an agent makes a biased hiring decision or approves a fraudulent transaction? What about when an agent maintains long-term memory across sessions, potentially leaking sensitive information?

Building a demo agent is easy. What’s hard is building an agent that handles edge cases, fails gracefully, and maintains performance under load.

The future of agentic AI

Agentic AI is moving from single-purpose tools to general-purpose systems. Instead of having separate agents for support, sales, and ops, companies are starting to build agent platforms that handle multiple use cases with shared infrastructure.

This is why multi-agent collaboration is becoming the standard architecture pattern. They condense complex workflows into specialized agents that work with each other. In other words, agents are evolving to mirror how human organizations work. Specialists collaborating, not generalists doing everything.

Want to build your own AI agent? Try Redis.

Redis provides everything you need to build production-ready AI agents: memory systems, vector search, and semantic caching in a single platform.

• Redis for AI: Ultra-fast vector database with sub-millisecond retrieval for RAG applications. When your agent needs to search millions of embeddings to ground its response, you need speed. Redis delivers it.
• Redis Agent Memory Server: Open-source dual-tier memory architecture for managing short-term and long-term agent memory. Short-term memory uses in-memory data structures for instant access. Long-term memory uses vector search for semantic retrieval across conversations.
• Redis LangCache: Fully-managed semantic caching service that recognizes when queries mean the same thing despite different wording. Cut your LLM API costs by 50-80% without changing your code.
• Native framework integrations: Redis works with LangChain, LangGraph, LlamaIndex, and 30+ other AI frameworks. Your agents can use Redis without custom integration work.

Ready to build? Here's where to start:

• Redis AI docs — Complete guide to building AI apps with Redis
• Agent memory guide with LangGraph — Hands-on tutorial for stateful agent memory
• Build your first RAG agent — Step-by-step RAG implementation
• Redis for AI homepage — Explore all AI capabilities

The infrastructure you choose determines what your agents can do. Choose infrastructure built for speed, scale, and production workloads.

FAQs about AI Agents

What is agentic AI? Is it the same as AI agents?

Yes, they're the same thing. "Agentic AI" and "AI agents" both describe AI systems that can perceive, plan, and act on their own to achieve goals. The term "agentic" just emphasizes that these systems work autonomously.

How do AI agents work?

AI agents follow a cycle: they take in information, plan what to do, search for relevant facts, execute actions through tools or APIs, evaluate the results, and respond. They learn from feedback to get better over time. Think of it like how you'd approach a complex task, but automated.

What can AI agents do?

AI agents can handle complex, multi-step tasks without you holding their hand. They can book meetings, write code, detect fraud, and process insurance claims. Unlike chatbots that just respond to what you say, agents can break down goals, use tools, make decisions, and get things done on their own.

What are AI agents used for?

You'll find AI agents anywhere decisions need to happen fast and autonomously. They automate customer service, help developers write code, catch fraudulent transactions, control robots, monitor patient health, and handle business processes. Basically anywhere you need systems that can think and act independently.

How do you use AI agents?

You use AI agents by giving them goals, not instructions. Instead of telling a system to search a database and then format the results, you just say "analyze Q3 sales performance." The agent figures out the steps and delivers results.

What is the difference between agentic AI and generative AI?

Generative AI creates stuff, be it text, images, code. Agentic AI takes action to get things done. An agent might use generative AI as one of its tools, but agents also call APIs, query databases, and execute multi-step workflows on their own.

How do you build an AI agent?

To build an AI agent, you need an LLM for reasoning, tools for the agent to interact with (APIs, databases), a memory system for context, and infrastructure for fast retrieval. Frameworks like LangChain, LangGraph, and CrewAI handle the heavy lifting. For production systems, you need fast vector search for RAG, semantic caching to control costs, and persistent memory. That's where Redis comes in.