Core Components of AI Agents

An AI agent, designed for complex, multi-step operations, isn't a single, indivisible entity. Instead, it's a system composed of several distinct, yet interconnected, components working in concert. Understanding these components is fundamental to effectively engineering prompts that guide an agent's behavior. Each part plays a specific role, from reasoning and decision-making to interacting with the external environment and remembering past events.

At a high level, these components enable an agent to perceive its environment (or input), reason about its goals, create plans, take actions, and learn from its experiences. Let's examine the typical building blocks.

The Core: Large Language Model (LLM)

At the heart of most modern AI agents lies a Large Language Model (LLM). This LLM serves as the primary reasoning engine or the 'brain' of the agent. It's responsible for:

• Understanding Instructions: Interpreting the user's goals and the directives embedded in prompts.
• Generating Text: Producing plans, explanations, intermediate thoughts, and final outputs.
• Making Inferences: Drawing conclusions based on the provided context, memory, and tool outputs.
• Driving Decisions: Often, the LLM output dictates the next action the agent will take, such as which tool to use or what sub-task to tackle.

The choice of LLM (e.g., models from OpenAI, Anthropic, Google, or open-source alternatives) significantly influences the agent's capabilities. Models vary in their proficiency at complex reasoning, instruction following, coding, and their susceptibility to generating unhelpful or incorrect information. Your prompt engineering strategies will often need to be tailored to the specific strengths and weaknesses of the chosen LLM foundation. For instance, some LLMs are better at following structured output formats (like JSON), which is important for tool use, while others might excel at creative text generation.

Planning and Execution Module (Controller)

While the LLM provides the raw intelligence, a distinct planning and execution module, often referred to as the controller or orchestrator, manages the agent's overall workflow. This module is typically a programmatic loop or framework that:

• Manages the Task Lifecycle: It takes a high-level goal (often from an initial prompt) and oversees its execution through multiple steps.
• Feeds Information to the LLM: It formats and presents information to the LLM, including the current state, available tools, and memory contents, often through carefully constructed prompts.
• Interprets LLM Outputs: It parses the LLM's responses to extract actionable commands, plans, or information. For example, an LLM might output a JSON object specifying a tool to call and its parameters.
• Executes Actions: It invokes tools, updates memory, or communicates results based on the LLM's guidance.
• Iterates: It repeats this cycle of prompting the LLM, interpreting its output, and taking action until the goal is achieved or a termination condition is met.

This controller is where much of the 'agentic' behavior is implemented. Popular agent architectures like ReAct (Reasoning and Acting) define specific ways this controller interacts with the LLM to interleave thought processes (reasoning) with actions. The design of this control loop and how it uses prompts is a central aspect of agent development.

Memory

For an agent to perform tasks that span more than a single turn or require knowledge past its immediate input, memory is indispensable. Memory allows an agent to:

• Maintain context across multiple steps.
• Learn from past interactions and avoid repeating mistakes.
• Store and retrieve information relevant to its current task.

We can generally distinguish between two types of memory in agent systems:

• Short-Term Memory (Working Memory): This refers to information the agent can access immediately, typically within the LLM's context window or a 'scratchpad'.

  • Context Window: The limited amount of text an LLM can consider at one time. Prompts must be designed to fit relevant current information here. As interactions grow, managing what stays in the context window becomes a significant challenge.
  • Scratchpad: A temporary area, often part of the prompt itself, where the agent can 'write down' intermediate thoughts, observations from tool use, or summaries of previous steps. Prompt engineering plays a direct role in managing what goes into and out of this scratchpad.

• Long-Term Memory: This enables an agent to retain and recall information over extended periods, past a single session or context window limit. Common implementations include:

  • Vector Databases: Store information (e.g., text chunks, user preferences, past conversations) as numerical embeddings. This allows for semantic search to retrieve relevant memories based on similarity to a current query or context. Prompts can instruct the agent on what to search for in the vector store.
  • Knowledge Graphs: Represent information as entities and relationships, enabling more structured queries and reasoning over stored facts.
  • Traditional Databases (SQL/NoSQL): For storing structured data the agent might need to access or update.

Prompts are essential for guiding how an agent uses its memory, for instance, instructing it to summarize previous steps for the scratchpad, to formulate queries for retrieving information from a long-term store, or to decide when and what information to commit to long-term memory.

Tools (and Tool Usage Mechanism)

LLMs, despite their impressive capabilities, have inherent limitations. They cannot directly access real-time information (their knowledge is frozen at the time of training), perform precise mathematical calculations reliably, or interact with external systems like APIs, databases, or the file system. This is where tools come in. Tools are external resources or functions that an agent can utilize to augment its abilities. Examples include:

• Web search engines (e.g., Google Search API, Bing Search API)
• Code interpreters (e.g., a Python execution environment for running generated code)
• Calculators or symbolic math engines
• APIs for specific services (weather, stock prices, e-commerce platforms, project management software)
• Custom functions defined by the developer (e.g., to interact with proprietary company systems)
• Database query interfaces

The agent's ability to use tools effectively is heavily reliant on prompt engineering. Prompts are used to:

1. Inform the Agent about Available Tools: Descriptions of tools, their capabilities, input parameters, and expected output formats are provided to the LLM, often in a structured way (like JSON Schema or OpenAPI specifications for APIs).
2. Guide Tool Selection: Based on the current task, the ongoing plan, and the context, the LLM decides which tool (if any) is appropriate to make progress.
3. Specify Tool Inputs: The LLM generates the necessary inputs or parameters for the chosen tool, adhering to the format described.
4. Instruct on Handling Tool Outputs: After a tool is executed by the controller, its output (e.g., search results, calculation outcome, API response) is fed back to the LLM. Prompts guide the agent in interpreting this output and integrating it into its ongoing reasoning process to decide the next step.

The mechanism for tool usage usually involves the planning and execution module parsing an LLM's request to use a tool (often expressed in a specific format like JSON), executing the tool with the specified inputs, and then feeding the tool's output back to the LLM for the next cycle of reasoning.

A high-level view of the interconnected components within an AI agent system. The controller orchestrates the flow of information and actions, with the LLM providing the core reasoning capabilities, memory offering context, and tools extending its abilities to interact with the external environment.

These core components, orchestrated effectively, allow an agent to tackle tasks that are far too complex for a simple, one-shot LLM query. As we proceed through this course, you'll learn how to use prompt engineering to influence each of these components, thereby shaping the agent's behavior, improving its reliability, and enabling it to perform sophisticated workflows.