In our previous post, we explored why you shouldn’t just build an agent, but give it a purpose. We covered the core principles that make an AI system truly “agentic,” moving beyond simple automation to tackle complex, goal-oriented tasks.
Now that you understand the “why,” it’s time to dive into the “how.” An agent is more than just a powerful LLM—it’s a complete system. To bring your purpose-driven agent to life, you need to assemble its core components:
- Brain: to think
- Eyes: to perceive the world
- Hands: to take action
- Mind: to coordinate it all. This post will break down these technical building blocks and show you how they fit together.
The “Brain”: The Core Reasoning Engine
At the heart of every agent is its “Brain”—the core LLM that does all the thinking, planning, and reasoning. This is the component that decomposes a complex goal into smaller steps and decides what to do next. The choice of LLM is the most critical decision in agent design, as it sets the upper limit for your agent’s intelligence.
When selecting your agent’s brain, you’re looking for a few key capabilities. The model must excel at complex reasoning to make smart plans. It needs to be exceptional at following instructions to the letter. To avoid making things up, it should be able to ground its knowledge in the facts you provide it. It also needs a sufficiently long context length (or “short-term memory”) to keep track of complex, multi-step tasks without getting lost. Finally, consider if the model has been specifically trained to excel at the tasks you want to automate, such as coding.
The “Eyes”: Seeing the World
An agent’s “Brain” is powerful, but by default, it’s blind. It can’t see the world outside of the text it’s given. Modern agents are overcoming this limitation with multimodal vision.
Many leading models can now understand more than just text. You can provide them with an image, a chart, or a screenshot, and they can see and interpret it. This gives the agent literal vision, allowing it to perform tasks like describing the contents of a picture, analyzing a graph of financial data, or understanding the layout of a web page from a screenshot.
The “Hands”: Taking Action with Tools
An agent that can only think and see isn’t very useful. To make a real impact, it needs “Hands” to interact with the digital world. These “Hands” are the tools the agent can use to perform actions. They generally fall into four categories.
- Grabbing Knowledge with RAG: Before taking action, an agent often needs to consult an external source of truth. Retrieval-Augmented Generation (RAG) is a tool that allows the agent to “grab” this knowledge. Think of it as the agent reaching for an open book during a test. It can look up relevant, up-to-date facts from a trusted source—like your company’s internal documents or a product database—to ground its subsequent actions in reality and prevent hallucinations.
- Executing Tasks with API Calls: This is like giving your agent a set of pre-built, specific tools for well-defined jobs. By calling an Application Programming Interface (API), an agent can perform concrete actions like sending an email, updating a customer record in a CRM, or booking an event on a calendar. This is how an agent reliably connects to and operates within existing business workflows.
- Solving Problems with Code Interpreters: This is like giving your agent a full workshop with raw materials. A code interpreter provides a secure, “sandboxed” environment where the agent can write and execute its own code (usually Python) to solve novel problems. It can perform complex calculations, analyze a dataset from an uploaded file, or generate a data visualization on the fly.
- Tapping Other Agents for Help: No single agent can be an expert at everything. A powerful action an agent can take is to recognize its own limitations and “tap” another agent for help. For example, a general-purpose assistant might receive a complex financial analysis request. Instead of trying to solve it alone, it can delegate the task to a specialized financial agent, which then returns the final analysis. This creates a collaborative system where agents act as a team, each contributing its unique skills. A truly capable agent needs a diverse toolkit to grab information, execute tasks, collaborate with others, and solve unique problems.
The “Mind”: Orchestration, Memory, and Control
The Brain, Eyes, and Hands are powerful components, but they need a “Mind” to coordinate them into a cohesive system. This is the job of the orchestration layer—the programming layer that handles the agent’s action loop.
The most popular orchestration framework is ReAct (Reason + Act), which operates on a simple but powerful Thought-Action-Observation loop. This forces the agent to “show its work” at every step:
- Thought: The agent thinks out loud, articulating its plan. (e.g., “Thought: The user wants to know the status of their order. I need to use the get_order_status tool with their order ID.”)
- Action: The agent executes the tool it decided on. (e.g., “Action: get_order_status(order_id=’12345’)”)
- Observation: The agent sees the result of its action. (e.g., “Observation: The order status is ‘Shipped’.”)
This loop makes the agent’s behavior transparent and debuggable. The “Mind” also includes the agent’s memory:
- Short-Term Memory: The agent’s immediate focus, held within the LLM’s context window. It’s what allows the agent to remember the last few things said in a conversation, but it’s temporary.
- Long-Term Memory: This gives the agent persistence, allowing it to recall information across different conversations and users. This is how an agent learns and becomes more personalized over time. Finally, the orchestrator needs to know when the job is done. Clear termination criteria—like reaching a “Final Answer” or a maximum number of steps—are crucial to prevent the agent from getting stuck in an infinite loop.
The Social Agent: Protocols for Collaboration
What happens when you have multiple agents, potentially built by different teams or companies? Without a common language, they can’t understand each other or work together. A new class of protocols is emerging to solve this problem:
- MCP (Model-Context Protocol): Think of this as the “USB-C port” for AI. It’s a standard that defines how an agent (the computer) connects to its various tools (the peripherals).
- A2A & ACP: These are competing standards that act as “universal translators” for agent-to-agent communication, allowing them to collaborate on complex tasks.
The Secret Sauce: Context Engineering
Having all the right components isn’t enough. The real art of building a high-performance agent lies in Context Engineering. An LLM’s context window—its short-term memory—is a finite and valuable resource. As you add more tools, conversational history, and retrieved knowledge, that window can quickly become cluttered, degrading the agent’s performance.
Context Engineering is the discipline dedicated to solving this. It is the strategic practice of curating and structuring the information provided to the agent at every step, ensuring the context window always contains the most relevant information for the task at hand.
To start learning about context engineering, here is a survey as of July 2025.
Conclusion: Building Your First Agent
A powerful AI agent is far more than just an LLM. It is a complete, cohesive system composed of a reasoning Brain, perceptive Eyes, active Hands, and a coordinating Mind.
By understanding these fundamental building blocks, you can move beyond simple prompting and begin to grasp the architecture of modern AI. You now have the foundational knowledge to think about how you might design and build your own intelligent, autonomous systems.
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