What are AI Agents?
Written by Massa Medi
Welcome to 2025 the year artificial intelligence transforms from clever chatbot tricks to true digital agents that can act, reason, remember, and adapt. In this in depth article, we’ll unravel what “AI agents” really are, why they’re such a game changer for businesses and individuals alike, and how the landscape of generative AI is shifting from single minded models to systems that orchestrate entire workflows. If you’re curious about the next big leap in AI or want practical examples of how these systems work, you’re in the right place.
From Monolithic AI Models to Flexible Compound AI Systems: The Generative AI Shift
Let’s begin by examining a critical shift underway in generative AI. In the early days, we relied on monolithic models large language models trained on swathes of data, but, crucially, limited to whatever information they absorbed at training time. This means they lack real world context, live data, and often struggle to adapt on the fly. To change their knowledge or improve their answers, you’d need substantial resources to retrain or fine tune those models.
To illustrate, imagine planning your summer vacation and wondering, “How many vacation days do I have left?” Feeding this query into a standard language model yields only an educated guess. The model knows nothing about you personally or Merge Society’s HR databases. You’d likely get an inaccurate answer.
Yet, traditional models still shine at more generic tasks: summarizing documents, generating email drafts, and pulling together first draft reports. The real magic, however, happens when we start building systems around models, integrating them into our organizational processes and leveraging their power alongside external data and programmable logic.
The Magic of Compound AI Systems: Integration Is Everything
Imagine a smarter approach to the vacation example: now the AI system doesn’t just guess, but actively queries the company’s HR vacation database. The process looks like this:
- The user’s question is sent to the language model.
- The model crafts a precise database search query (rather than simply responding with a guess).
- The system executes that query, fetches the correct vacation day number, and feeds it back to the model.
- The model then formulates a human readable answer: “Maya, you have 10 vacation days left this year.”
This modular approach combining models with external data, verification tools, and code is called a compound AI system. By breaking down tasks and choosing the right component for each, we get adaptable, powerful solutions that are far easier to configure than retraining giant models.
You might have heard the term Retrieval Augmented Generation (RAG) one of the most popular types of compound AI. In RAG, the system supplements the model’s knowledge by actively retrieving fresh content from databases or documents to answer queries more accurately.
Why Modular Systems Matter And Where They Fall Short
Compound systems excel at tasks that fit their carefully designed workflows. But they still have a critical limitation known as control logic the “path” a program follows to find an answer. For example, the vacation query system can only answer questions by checking the vacation policy database. If you suddenly ask about the weather in Florida, the system will fail. Why? Because its logic is programmed to always search the vacation database, and nowhere else it simply can’t adapt outside of its narrow path.
Here, control logic acts as the brain of the system, explicit and static it decides which component runs and when, as prescribed by the human programmer. That’s effective for predictable, well defined tasks; but what if you want something more… autonomous?
Enter the AI Agent: Putting Large Language Models in Charge
The innovation that defines 2025? Giving the large language model (LLM) greater autonomy, letting it decide how to solve the problem based on its own advanced reasoning. These AI agents can break down complex problems, formulate plans, and dynamically decide which tools, databases, and actions to use as they work toward a solution even for tasks they’ve never seen before.
Picture a spectrum of system autonomy: on the left, strict programs act instantly and never think outside their script (“think fast, follow instructions, never improvise”). On the right, you have agentic systems designed to “think slow”, plan, experiment, iterate, and revise their approach until they succeed. Just like a human problem solver, AI agents may break complex tasks into steps, get stuck, readjust, and draw on outside resources as needed.
Anatomy of an AI Agent: Reasoning, Acting, and Remembering
So what makes up an AI agent, specifically one based on a state of the art LLM?
- Reasoning: The LLM sits at the core of the agent, prompted to formulate plans, break down multi step processes, and reason through each phase not just spit out the first answer that comes to mind.
- Action (via Tools): Agents can invoke external programs (known as tools) to execute steps. These might include APIs to search the web, access company databases, run calculations, manipulate spreadsheets, or even call other models (for example, to translate text).
- Memory: AI agents build up logs of their internal “thinking,” as well as the history of conversations they’ve had with users. This memory enables context aware decision making, more personalized responses, and the ability to learn over time from previous interactions.
A combination of these capabilities reasoning, acting, and remembering brings us to a powerful paradigm: the React agent. (Not to be confused with the JavaScript framework; here, React means “Reason and Act.”)
The React Agent in Action: A Real World Vacation Planning Example
To see this all in action, let’s return to our enthusiastic vacation planner. Imagine you want AI to answer:
“How many two ounce sunscreen bottles should I bring to Florida next month, considering I plan to spend lots of time outdoors?”
This problem is surprisingly complex. Here’s how a React agent approaches it, step by step:
- Determine how many vacation days you’re taking (retrievable from memory, given you previously asked about vacation days).
- Estimate how many hours you’ll spend in the sun (perhaps by checking Florida weather forecasts for next month and calculating average sunshine hours).
- Find recommended sunscreen dosage per hour (accessing public health recommendations online).
- Do the math: Combine all collected data to determine how many two ounce bottles you’ll need for your trip.
What’s powerful here is the modularity and flexibility of the agentic system: it can dynamically decide which tools and data sources to use, iterate its plan based on what it finds, correct itself if it encounters errors, and explore multiple paths to get the best solution.
Choosing Between Programmatic and Agentic Systems: When to Use Each
With all this flexibility, should we make every AI system agentic? Not necessarily. There’s a trade off between adaptability and efficiency.
- For narrow, well defined problem sets (like only answering vacation policy questions), a programmatic compound system is more efficient. It follows the same path each time, ensuring predictable, fast answers the best fit where creativity and adaptability aren’t needed.
- For broad, open ended, or novel challenges (like solving diverse GitHub issues, handling both vacation queries and Florida weather questions, or troubleshooting multi part processes), agentic systems shine. They can adapt, explore new paths, and figure out solutions that weren’t explicitly programmed in.
As 2025 unfolds, we’re witnessing a rapid evolution: compound systems are growing ever more agentic. The future belongs to AI tools that can not only answer questions, but dynamically plan, reason, adapt, and learn. For now, most systems will still include a human in the loop to check outputs and guide accuracy but as the technology improves, agents themselves will only get smarter and more capable.
Final Thoughts: The Agentic Future Is Here
AI agents aren’t a science fiction fantasy they’re arriving now, powered by breakthroughs in large language models, modular system design, and flexible integration with tools and memory. The line between rigid programs and adaptable agents is shortening, and this means more personalized, context aware, and agile digital assistants in every field.
Stay tuned for deeper dives into building and applying these systems across business, productivity, and everyday life. Don’t forget to subscribe for more insights as the AI agent revolution continues!
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