The Agent, Tool, and Toolkit Architecture

While chains execute a predetermined sequence of steps, agents operate on a dynamic loop of reasoning and action. This behavior is not managed by a single, monolithic object but by a collaborative architecture of three primary components: the Agent, the Tools it can use, and the Runtime (often built with LangGraph) that orchestrates the entire process. Understanding how these pieces interact is fundamental to building effective agentic systems.

The Agent: The Reasoning Engine

At its core, the agent is the decision-making component. However, it's not a standalone piece of code in the way a Python class is. Instead, the "Agent" in LangChain is an abstraction that combines a language model with a prompt and defined tool schemas. This combination transforms a general-purpose LLM into a reasoning engine capable of driving a task forward.

The prompt instructs the LLM to follow a specific thought process, often referred to as a "reasoning loop." A common pattern, used by frameworks like ReAct (Reasoning and Acting), asks the model to break down its thinking into distinct steps:

• Thought: The agent's internal monologue, where it analyzes the current situation, assesses progress towards the goal, and decides what to do next.
• Action: The specific tool the agent decides to use to make progress. Modern LLMs with Tool Calling capabilities generate this as a structured object rather than raw text.
• Action Input: The parameters or query to pass to the chosen tool.
• Observation: The result returned from executing the tool, which the agent will use in its next "Thought" step.

The LLM generates output that follows this structure. The agent's sole responsibility is to produce the next thought and action based on the history of previous actions and observations.

Tools: The Agent's Capabilities

If the agent is the brain, tools are its hands. A tool is a function or service that an agent can call to interact outside the LLM. Anything an agent needs to do, from searching the web to querying a database or calculating a number, is exposed as a tool.

Each tool is defined by two important attributes:

1. Functionality: The actual code that gets executed. This could be a wrapper around a public API, a Python function, or even another LangChain chain.
2. Description: A clear, natural language description of what the tool does, what its inputs should be, and what it returns.

The description is immensely important. The agent's LLM has no inherent knowledge of the tool's code; it relies entirely on the description to determine which tool is appropriate for a given task. A well-written description that accurately reflects the tool's purpose and input format is the difference between an agent that works effectively and one that consistently fails.

For example, a SearchTool might have a description like: "Useful for when you need to answer questions about current events or look up information on the internet."

Toolkits: Organizing Capabilities

A toolkit is simply a collection of tools designed to work together to accomplish tasks in a specific domain. Rather than loading tools one by one, you can load a toolkit that provides a pre-configured set of capabilities.

For instance, LangChain provides a SQLDatabaseToolkit which includes tools for:

• Listing tables in a database.
• Inspecting the schema of a specific table.
• Executing a SQL query.
• Checking a SQL query for syntax errors.

Using a toolkit simplifies setup and ensures the agent has a coherent set of related capabilities for interacting with a particular system.

The Runtime Loop

The runtime environment is the system that brings all the components together and drives the agent's operation. It is responsible for executing the loop that makes an agent autonomous. While the AgentExecutor class was the historical way to manage this loop, modern LangChain applications typically use LangGraph to define this orchestration. This allows for greater control over state and execution flow.

Here is the step-by-step execution flow managed by the runtime:

1. It receives an initial input or objective from the user (e.g., "What was the score of the last Super Bowl and who was the MVP?").
2. It invokes the Agent (the LLM with its prompt/tools), passing the input and any previous steps.
3. The Agent reasons and outputs the next Action to take (e.g., a structured tool call for "search") and the Action Input (e.g., "Super Bowl LVII score and MVP").
4. The runtime parses this output to identify the tool and its input.
5. It calls the specified tool with the provided input.
6. The tool executes and returns a result, which is formatted as an Observation (e.g., "The Kansas City Chiefs defeated the Philadelphia Eagles 38-35. Patrick Mahomes was the MVP.").
7. The runtime takes this Observation and updates the state (history) of the interaction.
8. It repeats the process, passing the updated history back to the Agent for the next reasoning step. The agent might see the result and decide its task is complete.
9. Once the Agent responds with a "Final Answer" or text message instead of a tool call, the runtime stops the loop and returns this final response to the user.

The following diagram illustrates this interactive flow between the components.

The agent runtime loop. It coordinates the interaction between the user, the reasoning agent (LLM), and the available tools until a final answer is produced.

This modular architecture allows for great flexibility. You can swap out the LLM, modify the agent's prompt, or add new custom tools without changing the underlying runtime logic. In the following sections, we will put this architecture into practice by equipping an agent with both pre-built and custom tools to solve problems.