Agent Architecture: Components Interaction and Agent Memory
Learn how the parts of an AI agent work together to gather information, remember past steps, and respond to the world around it.
In the previous lesson, we explored the three core components of an AI agent: the model that reasons, the tools that perform actions, and the instructions that guide behavior. But a functioning agent needs more than a reasoning engine and some tools. If we want agents to behave intelligently across time and context, they must also perceive their surroundings and remember what has happened.
In this lesson, we’ll expand our view from individual components to complete agent systems. We’ll explore how agents gather input through environmental sensing, how they maintain continuity with memory, and how these systems fit into the overall workflow that drives interaction over time.
By the end of this lesson, we will be able to:
Identify how memory systems and perception extend basic agent functionality.
Understand where memory and sensing occur in the agent’s reasoning loop.
Explain how external data retrieval, such as with vector databases, supports long-term behavior.
Recognize how real-world agents use these capabilities to respond adaptively and intelligently.
The architecture blueprint
We can think of the agent architecture as a control flow that connects four primary elements of functioning agents:
Input interface: The agent receives information from the environment or a user. This can include text prompts, sensor data, API signals, or even multimodal inputs like audio or images.
Reasoning core (the model): The language model interprets the input, plans the next steps, and determines which tools or actions to use. This step involves both logic and language understanding.
Tools and actions: Based on its reasoning, the agent may call APIs, fetch documents, send responses, or trigger physical actions if it is connected to hardware.
Memory and context: To maintain continuity, the agent may store or retrieve past information, such as conversational history, retrieved documents, or long-term user preferences.
This architecture directly builds on our previous lesson’s core components: the reasoning core is powered by the model and guided by the instructions (often through a system prompt). Meanwhile, the input interface and memory provide the necessary context for it to operate effectively and interact with the world.
While these four elements form the foundational control loop, advanced agentic systems often incorporate additional specialized or layered components for specific functions. These include robust safety layers or detailed monitoring interfaces. These build upon the core architecture to enable more sophisticated and reliable behaviors, which we will explore in later lessons.
The illustration below shows how these elements interact in an agentic system:
This system operates in a loop. The agent receives input, reasons, acts, observes the outcome, and continues. Each part reinforces the ...