Agent Architecture: Core Agent Components
Explore the three essential parts of every AI agent: the model, the tools it uses, and the instructions that guide it.
Agents are not black boxes. They are systems made from distinct, configurable components. These components are the fundamental building blocks for designing robust, and scalable agentic AI systems. At the center are three foundational elements:
The model, which interprets inputs and reasons through decisions.
The tools, which let the agent interact with external systems.
The instructions, which guide how the agent behaves, communicates, and prioritizes goals.
By choosing and combining these elements thoughtfully, we can shape an agent to fit a specific task, domain, or environment. In this lesson, we will explore each component in depth. We’ll examine what role it plays, how to select or design it, and how it contributes to the overall system.
By the end of this lesson, you will be able to:
Explain model selection factors.
Describe how tools enable agent action.
Recognize the role of instructions in agent behavior.
Understand component interaction within an agent system.
The model: Choosing the agent’s brain
The model sits at the center of the agent’s decision-making process. Given some input, such as a user prompt or an observed event, the model can understand what the user is asking or what the situation requires, decide what steps or actions should be taken to achieve a goal. It can choose how to proceed, either by taking a specific action, or by invoking another tool. In more advanced agents, the model may also evaluate whether its past decisions were successful.
The idea of a model evaluating its own decisions is a powerful technique often called reflection or self-critique. Advanced agents can be designed to critique their own plans or tool outputs, and then loop back to correct them. This makes them far more robust and adaptive. We’ll explore this technique in detail while exploring real-world agentic systems.
In most agents today, the role of a model is filled by LLMs. Large language models work well as agent brains because they are:
Flexible: They can understand a wide variety of tasks expressed in natural language.
Compositional: They can break tasks into subtasks and generate coherent, multi-step reasoning chains.
Adaptable: They generalize well to new domains without retraining.
For example, when you give an agent the prompt:
“Book me the ...