Building An AI Agent : Framework & Tools You Need

June 23, 2025.

AI agents are increasingly being adopted by Fortune 500 organizations and startups alike. We covered the foundation of AI agents in part I of this series and now we will cover the framework and technology stack you can use to build these agents. 

Students often ask, isn't building an AI agent similar to building or even training an LLM-powered chatbot? The short answer here is no. One way to think about this question is to understand that you're no longer trying to optimize predictions—i.e. responses—you're architecting an autonomous software system that thinks, plans, and acts over time. You’re building a self-directed software agent that can interpret a goal, break it down into steps, take actions, integrate with other tools, and execute on the goal end-to-end across domains.

All of this needs to happen in a fully automated and iterative manner. So the question is what enables these agents to do so? It all boils down to a standardized framework or simply put, the core components that form the building block of these AI agents. If you want to build an AI agent, it helps to start with this framework.

Framework - Core Components of AI Agent

The first component you will need to define is the agent’s operating environment. Where will it operate and act? That could be a browser, a database, a suite of internal APIs, or even a physical robot. Once the environment is scoped, you define the agent’s goal—what do you want it to accomplish autonomously. This could be something as narrow as generating a quarterly financial report for a startup or as broad as designing a marketing campaign for a series of products a company is about to launch.

Next, you need a planner. This is the logic layer that translates the high-level goal you just defined for your agent into a series of executable steps. Often, the planner is powered by a large language model (LLM) that uses chain-of-thought reasoning to break that goal down into actionable steps. This is just a fancy way of saying it can think out loud and break problems into pieces. But it doesn’t stop there. The planner also monitors execution, checks whether each step succeeded, and adjusts the plan dynamically if things go off track. 

But planning isn’t enough. The agent also needs memory. Without memory, it’s like a goldfish—can’t remember what it just did, can’t learn from its mistakes. It's basically stateless. 

Memory enables it to track what’s already been done, what failed, and what it still needs to do. It needs both short-term and long-term memory. Short-term memory allows the agent to track progress within a single task or session—what steps were taken, what succeeded, what failed. Long-term memory, on the other hand, helps it build experience over time or across sessions. Memory, in simple words, is the divine ingredient that enables context-awareness and continuous learning in these agents.

Once the agent knows its goal, the corresponding plan, and is configured  with the required memory, it needs the ability to take action. That’s where tools come in. Your agent should be able to call APIs, read and write files, navigate web pages, trigger workflows, or send messages—whatever tools are relevant to its domain. These tools act like extensions of the agent’s body in the digital world.

At the heart of it all sits the reasoning engine—typically a large language model—that interprets the goal, decides on an execution plan, and adapts based on feedback from the environment. And finally, you’ll need a loop. The agent should operate as a closed loop system: observe, plan, act, assess, repeat—until it either succeeds, fails gracefully, or asks for help.

Now that we understand the core components, we need to understand the technology stack that can be used to build these AI agents.

What’s Under the Hood: Technology Stack

At the core of an AI agent is your LLM of choice—the brain of the agent. This brain is powered by LLMs like GPT-4, Claude, LLaMA, or Mistral. This serves as the agent’s cognitive core or the reasoning engine that takes the end goal from the user, interprets this goal, generates and executes step-by-step plans to meet that goal. This includes  interpreting data received from APIs or other agents and generating responses or actions the agent takes to move things forward.

But reasoning alone isn’t enough. Most agents also need the ability to interact with the outside world. Specifically, they must be able to decide when and how to interact with external tools—like a search API, a payment gateway, or a database. This capability, often referred to as function calling in the world of AI agents. 

You might ask: what if an agent needs to communicate with a software system or web app that doesn't have an agent-compatible API? When structured APIs aren’t available, agents often fall back on browser automation tools like Playwright or Puppeteer. These tools give the agent the ability to interact with websites just like a human—clicking buttons, navigating pages, or scraping content. This is particularly useful for agents operating in the real-world outside the test environment. So what does it do with that data received from these external software systems or databases? 

Well, it needs to store that data and take action—and it does so in an iterative manner. That means it needs memory.

Memory & Vector Embeddings

For an agent to be truly valuable, it needs memory—both short-term,  to retain context within a session, and long-term, to retain context across sessions. This is typically implemented through vector databases such as Chroma, Pinecone, or Weaviate. These systems store information as high-dimensional embeddings, allowing for semantic recall—meaning the agent can remember and recall contextual information across sessions. You might ask what is high-dimensional embedding? 

When we say information is stored as high-dimensional embeddings, we mean that instead of storing words or data as plain text, the agent converts them into mathematical representations—vectors with hundreds or even thousands of dimensions. Each embedding captures the meaning and context of the original input. 

To simplify this, let’s take an example. Suppose the user interacting with an AI agent dedicated to travel planning says: “Book a flight to Berlin for this week.” The agent converts this into a vector—a long list of numbers like: [0.12, -0.83, 0.57, ..., 0.03]. Now, consider a second request: “Book a flight to Berlin for next week.” This too gets converted into a vector—something like: [0.15, -0.80, 0.60, ..., 0.01].

To a traditional database, these two entries would look like separate, unrelated strings. But a vector database understands that these two vectors are close to each other in high-dimensional space. Why? Because they share semantic meaning—both refer to booking a flight to the same city, with only a small difference in timing.

This proximity allows the agent to recognize that the two requests are related, even if worded differently. That’s what makes vector databases powerful—they don’t just store data, they also store context. The goal here is to ensure that an agent remembers all the attempts it made to complete a specific task and learns from them so it doesn't repeat its own mistakes.

LangChain & Multi-Agent Systems

Now, to orchestrate all these components—reasoning, memory, and tools—developers need a solid framework. That’s where LangChain comes in. It remains the most widely adopted framework in the world of AI agents. It provides a standardized approach to visualize and organize all the components you will need to build an AI agent, namely - LLM, tools, memory systems, and control logic. You might ask well, what about programs or goals that need multiple agents? How do we handle that? 

That’s a valid question. Certain tasks are simply too complex or multi-faceted for a single agent to handle efficiently. So, in these cases, instead of building one generalist agent, we divide the system into multiple specialized agents, each playing a distinct role. For example, one agent may act as a planner—mapping out the overall strategy to complete a task. Another might serve as a researcher, gathering the necessary information or data. A third agent may take on the role of executor, responsible for actually carrying out the task, such as making API calls or writing files. This multi-agent approach allows us to make these systems modular and scalable. But that brings up an important question: How do these agents collaborate with each other in such a system?

To make this multi-agent approach effective, frameworks like CrewAI and Microsoft Autogen are used. These frameworks act as coordinators, managing how tasks are assigned to each agent, how information flows between agents, and how each agent’s output feeds into the next step. 

They also maintain a shared memory or working context, ensuring that all agents stay aligned and operate with the same understanding of the task. They also handle dependencies—making sure, for example, that the executor doesn’t act until the researcher has gathered the necessary data and the planner has mapped out the steps. This coordination is what makes multi-agent systems cohesive and powerful. 

As agents become more autonomous, the need for self-evaluation and feedback loops becomes critical. Just like us humans, we think about what we say, how we say it, and why we say what we say. We analyze—either in real time or after the event—our actions so we can catch our mistakes and learn from them. Wouldn’t we want these AI agents to do the same?

Many modern architectures now include built-in mechanisms for self-evaluation—where the agent critiques and optimizes its own output. This might involve a second pass by another LLM, a scoring function based on defined metrics, or reinforcement learning that evaluates the output of the agent and provides feedback. In domains where errors carry significant risk—like finance, healthcare, or law—human-in-the-loop review is added for oversight, ensuring the agent escalates decisions when appropriate.

Infrastructure

Finally, production-ready agents require a solid infrastructure layer to operate reliably at scale. This means, how you package these agents matters. Each of these agents are bundled with corresponding software dependencies using Docker containers. In simple terms, this means that if you're a developer, you can download the agent as a single Docker file and run it on your server without worrying about installing multiple packages or resolving dependency conflicts. This also allows us to scale these agents, duplicate and deploy 1000s of these digital containers in the cloud environment. 

You might ask: what about the reliability arm? How do we ensure that these agents don’t fail silently or spiral into loops? That’s where emerging agent monitoring tools come in. These tools are being developed to detect agent-related failures, prevent infinite loops, and monitor behavior to ensure overall system reliability. Are these the next-gen SRE (Site Reliability Engineering) agents? It's early to say for sure, but the direction is clear—we’re moving toward systems where agents will monitor and manage other agents, across the broader product development and testing life cycle.

So while the idea of an AI agent might feel futuristic, the build process is reasonably matured and standardized. So where do we go from here?

Whether you're a student, engineer, founder, or working professional, one thing is certain—sooner than you think, a part of your job will be handled by an agent. Not because it’s a trend, but because it makes sense. Just like Slack and MS Teams quietly became part of every workplace, these agents will show up. That shift is inevitable.

So, What Should You Do Next?

There are two core paths you can evaluate for yourself. 

Path One: Can you think of a real-world problem that you—and at least ten people you know—struggle with frequently? Now zoom out and ask yourself: are there potentially millions of people facing that same problem? If the answer is yes, and you believe AI agents can solve it, you’re already on the path of a builder. 

If that’s not your path, you may want to consider the second route.

Path Two: Is there a problem within the organization you currently work for that could be solved using agents? Can you validate that it’s worth solving—maybe because it costs the company time, money, or customer trust on a recurring basis? If solving it could create measurable value—whether through efficiency, reliability, or improved user experience—then you’re on the path to become an agent-powered innovator inside your company.

Either path puts you ahead of the curve. The ability to understand and build with agents is going to compound in value over the next few years.

The best time to take action was yesterday.  The second-best time is now.

Whatever you do, start small. Start now.

Make your summer count.

Cheers,

Prathamesh

Disclaimer: This blog is for educational purposes only and does not constitute financial, business, or legal advice. The experiences shared are based on past events. All opinions expressed are those of the author and do not represent the views of any mentioned companies. Readers are solely responsible for conducting their own due diligence and should seek professional legal or financial advice tailored to their specific circumstances. The author and publisher make no representations or warranties regarding the accuracy of the content and expressly disclaim any liability for decisions made or actions taken based on this blog.