Hey everyone! 2025 is shaping up to be the year of AI Agents. You’ve probably heard a lot about Generative AI, but things are evolving fast. Let’s break down what AI Agents are, why they matter, and how they’re different from what came before – without getting bogged down in technical stuff.
Why AI Agents Are a Big Deal (Think “Digital Assistant”)
Imagine having a smart digital assistant that understands what you need, thinks about the best way to help, and then acts to get the job done. That’s the power of an AI Agent.
Think of it this way:
- AI (Artificial Intelligence): The raw brainpower, the potential.
- AI Agent: The one who actually gets stuff done. It’s like giving that genius a task and saying, “Okay, now go do something useful with all that brainpower!”
While AI is all about smarts, an AI Agent is the practical, hands-on version. It’s the difference between knowing how to do something and actually doing it.
From One Big Model to Intelligent Systems
At first, we had these giant AI models that could do some cool things. But they were limited. They only knew what they were trained on, and it was hard to make them adapt to new situations. It was like having a super-smart person who only knew about one subject.
Example: Imagine asking one of these models to plan your summer vacation and tell you how many vacation days you have left. It probably wouldn’t know who you are or have access to your work records, so the answer would likely be wrong.
These models are great for things like:
- Summarizing documents
- Writing first drafts of emails
But the real magic happens when we build systems around these models.
Compound AI Systems: Working Together (But Still a Bit Rigid)
Instead of relying on a single model, we can create systems that connect the model to other tools and data sources. This is like giving that super-smart person access to the internet, a calculator, and your personal files!
Let’s revisit the vacation example:
- You ask the system to plan your vacation and tell you how many days you have.
- The system uses the AI model to create a search query (e.g., “vacation days for [Your Name]”).
- The system searches your company’s vacation database.
- The database provides the answer (e.g., “Maya, you have ten days left”).
- The AI model uses that information to generate a complete sentence: “Maya, you have ten days left in your vacation database.”
The result? A correct and useful answer!
This is a compound AI system. It’s like a team of specialists working together to solve a problem. These systems are:
- Modular: Made up of different components (AI models, databases, programs).
- Adaptable: Easier to update and improve than a single, giant model.
One popular type of compound AI system is Retrieval Augmented Generation (RAG).
Here’s the catch: Most of these systems have a fixed “path” for answering questions. If you ask it something outside of that path (e.g., “What’s the weather like?”), it might fail because it’s only designed to search your vacation policy. This “path” is called the control logic.
AI Agents: The Brains in Control (More Flexible and Autonomous)
So, how do we make these systems more flexible? That’s where AI Agents come in. Instead of a human defining the control logic, we put a Large Language Model (LLM) in charge.
LLMs have gotten really good at reasoning. We can give them complex problems and ask them to:
- Break the problem down into smaller steps.
- Create a plan to solve each step.
- Adjust the plan as needed.
Think of it like this:
- Compound AI System: “Think fast, act as programmed!”
- AI Agent: “Think slow, create a plan, and figure out the best way to solve this!”
When we let an LLM control the logic, we’re creating an agentic approach.
What Makes Up an AI Agent?
AI Agents have three key capabilities:
- Reasoning: The ability to analyze a problem and create a plan.
- Acting: The ability to use external tools to execute the plan.
- Memory: The ability to remember past interactions and use that information to personalize the experience.
Tools are external programs that the AI Agent can use. Examples include:
- Search engines (for searching the web)
- Databases
- Calculators
- Other AI models (for tasks like translation)
- APIs
One popular way to configure AI Agents is through something called ReACT, which combines reasoning and acting.
Vacation Planning: The Agentic Way
Let’s go back to the vacation example, but this time with an AI Agent:
You ask: “I’m going to Florida next month and plan to be outdoors a lot. I burn easily. How many two-ounce sunscreen bottles should I bring?”
The AI Agent might:
- Remember that you’ve asked about vacation days before (using its memory).
- Check the weather forecast for Florida next month to estimate the average sun hours (using a search tool).
- Consult a public health website to find the recommended sunscreen dosage per hour (using a search tool).
- Calculate how much sunscreen you’ll need and how many two-ounce bottles that equals (using a calculator tool).
- Provide you with a final answer.
The great thing is that there are many ways to solve this problem, and the AI Agent can explore different paths to find the best solution. It’s not limited to one fixed “path.”
The 1-2-3 of AI Agents
Let’s break down how AI agents work in a simple way:
- Understand: AI Agents listen to what you’re asking or needing.
- Think: They process this information and figure out the best way to help you.
- Act: Finally, they deliver what you need, whether it’s an answer, a solution, or help completing a task.
A Four-Step Illustration
Here’s a more detailed look at the process:
- Input: You ask a question or give a command.
- Processing: The AI Agent thinks about what you’ve asked and searches for the best way to respond.
- Action: The AI Agent gives you an answer, finds information, or performs a task.
- Learning: The more you use it, the smarter it gets, learning from past interactions to improve.
The Interface and the Workflow
When we talk about what an AI Agent looks like, it’s helpful to think of it as a combination of two things:
- Interface: This is how you interact with the AI Agent – maybe through a chat window, a voice assistant, or even a button on a website.
- Workflow: Behind that interface is where the real magic happens. Imagine a flowchart. You ask the AI Agent a question or give it a command through the interface. That’s the starting point. From there, the AI Agent follows a series of steps or nodes in a workflow. These nodes could involve accessing information from a database, making a decision based on what it finds, or even performing a task like sending an email.
The Impact on Businesses (Especially Local!)
AI Agents are about to trigger a seismic shift in how we live and work, especially for local businesses and those who support them.
Imagine AI handling routine tasks, freeing business owners to focus on growth and innovation. For local businesses, this could mean smarter customer service and more efficient operations. Agencies and service providers can deliver more value with less effort.
Why This Matters: A Major Transformation
This isn’t just like another tech upgrade; it’s likely the biggest transformation we’ll see in our lifetime. Those who embrace AI Agents now will have a major advantage, while those who don’t risk being left behind in a rapidly changing world.
The Future of AI: More Autonomy (But Humans Still Matter!)
Compound AI systems are useful, but AI Agents are the future. It’s all about finding the right balance between AI autonomy and human control.
- Narrow Problems: If you have a well-defined problem, a compound AI system might be more efficient because it follows a fixed path. (Example: Always searching a specific database for one type of answer).
- Complex Problems: If you need a system to handle a wide range of queries and tasks (e.g., solving GitHub issues, providing comprehensive customer support), an AI Agent is a better choice because it can adapt and figure out the best approach.
We’re still in the early days of AI Agents, but the progress is rapid. By combining system design with agentic behavior, we’re creating AI that’s more powerful and versatile than ever before. And of course, humans will still be in the loop to ensure accuracy and safety!
One expert predicts that someday there will be thousands of agents available and that we’re going to live in a world where there are going to be hundreds of millions of billions of different AI agents – eventually, probably more AI agents than there are people in the world!