Build your first Java agent with Google ADK

Java Agent Development Kit (ADK) Summary

Key Concepts:

• Agent Development Kit (ADK): A flexible, modular, open-source framework for simplifying the development and deployment of AI agents.
• LLM-based Agents: Agents that use Large Language Models (LLMs) for reasoning and decision-making.
• Workflow Agents: Agents designed for prescriptive or deterministic logic flows.
• Custom Agents: Agents where developers can implement their own logic.
• Tools: Mechanisms for agents to interact with the external world (e.g., accessing the internet, databases, APIs).
• Function Calling: An LLM capability that allows agents to use tools by determining when an external action is needed and which tool to use.
• Agent Tool: Using one agent as a tool within another agent, enabling hierarchical or collaborative systems.
• Multi-Agent Systems: Systems where multiple agents collaborate to solve complex problems.
• Model Context Protocol (MCP) and Agent-to-Agent (A2A) Protocol: Protocols supporting extensibility and communication in multi-agent systems.
• Sessions: Manage multiple conversations for different users and applications.
• State: An agent's temporary scratchpad for storing data specific to a particular conversation.
• Events: Used to update the state programmatically.

Getting Started with Java ADK

1. Dependency Inclusion: Add the ADK dependency to your project's build file (Maven or Gradle).
2. Dev UI (Optional): Include the -ashdev dependency for a web-based UI to prototype and debug agents.
3. Agent Definition: Define your agent using the AgentBuilder, providing a name, description, LLM model, and instructions.
4. Running the Agent:
   • Programmatically: Use an in-memory runner, create a session, and implement a loop to take user input, execute the agent using runner.runAsync(), and process the asynchronous events to form a response.
   • Dev UI: Run the Dev UI from the directory containing your agent sources and interact with the agent through the web console.

Agent Types

• LLM-based Agents: Utilize LLMs for reasoning and decision-making. Example: A science teacher agent that answers questions about scientific concepts.
• Workflow Agents: Designed for deterministic logic flows. Subtypes include:
  • Sequential Agents: Chain sub-agents together, where the output of one becomes the input of the next.
  • Parallel Agents: Execute sub-agents in parallel.
  • Loop Agents: Run a sequence of agents in a loop until a specified number of iterations or a termination condition is met.
• Custom Agents: Allow developers to code their own logic.

Tools: Connecting Agents to the External World

• Purpose: Enable agents to interact with external resources like the internet, databases, and APIs.
• Function Calling: LLMs analyze user requests, determine if an external action is needed, and select the appropriate tool with its arguments.
• Types of Tools:
  • Function Tools: Wrap a piece of code (a method) and make it available to the agent.
  • Built-in Tools: Provided by the ADK (e.g., Google Search).
  • Third-Party Tools: Provided by external services (e.g., MCP servers).
• Function Tool Implementation:
  1. Define a Java method to serve as the external function.
  2. Annotate the method with @Schema annotations to document its purpose and parameters.
  3. Return a structured response (e.g., a map) that will be serialized into a JSON object and passed back to the LLM agent.

Multi-Agent Systems

• Agent Tool Concept: Allows one agent to use another agent as a tool. Example: A main agent uses a summarizer agent to create summaries of long text.
• Workflow Agents: Essential for orchestrating complex flows involving multiple agents.
• Benefits: Modularity, easier development and maintenance for complex problems.

Context and Knowledge Management

• Sessions: Manage multiple conversations for different users and applications. Key attributes:
  • Unique ID
  • Application Name
  • User ID
  • History of interactions (messages, tool calls, results)
  • Shared State Map
  • Last Event Timestamp
• State: An agent's temporary scratchpad for storing data specific to a particular conversation.
• State Management:
  • Specify the initial state when creating the session.
  • Access the state within agent instructions using placeholders (e.g., {subject}).
  • Update the state programmatically using events (send a delta to describe the changes).

Example: Science Teacher Agent

• Purpose: To answer questions about scientific concepts.
• Implementation:
  • Defined using AgentBuilder with a name, description, LLM model, and instructions.
  • Can be made more generic by accessing the session state to determine the subject and audience.

Example: Customer Agent

• Purpose: To allow customers to ask about the status of their orders.
• Implementation:
  • Uses a function tool to wrap a method that retrieves order status.
  • The method is annotated with @Schema to describe its purpose and parameters.

Key Arguments and Perspectives

• Building AI agents from scratch can be complex due to managing state, tool calls, and control flows.
• ADK simplifies agent development by providing a flexible and modular framework.
• Tools are essential for enabling agents to interact with the external world.
• Multi-agent systems allow for solving complex problems by decomposing them into smaller subtasks.
• Proper context and knowledge management are crucial for handling complex ongoing interactions.
• Precise and explicit instructions are critical for defining the agent's purpose, persona, and goals.

Conclusion

The Java ADK provides a comprehensive set of tools and frameworks for building sophisticated AI agents. By leveraging LLM-based agents, workflow orchestration, multi-agent collaboration, and robust state management, developers can create powerful solutions for a wide range of applications. The ADK simplifies the development process, promotes modularity, and enables the creation of more capable and intelligent agents.