Building Agents with Google ADK and Vertex AI

Get to know your course instructors, set up GCP resources, and get an overview of the course.

Extend AI agents beyond text with tool integrations, enabling reliable real-time actions and data access.

Learn to extend LLM agents with ADK and Gemini for tool usage—integrate and register custom functions, handle errors, and guide agents with effective prompts for real-world tasks.

Discover structured outputs in AI: transform responses into actionable JSON for integration. Utilize schemas, parsers, and function calls to enhance reliability and automation in workflows.

Learn to generate structured outputs with Vertex AI Gemini and Pydantic, enabling reliable data extraction and downstream processing using defined schemas and robust error handling.

Explore agent state management with state machines. Learn how agents track user input, instructions, and tool use for complex workflows, ensuring adaptability and reliability.

Learn to manage agent state using ADK through demonstrations, hands-on exercises, and quizzes for effective agent development.

Explore short-term memory in AI agents, enhancing coherence via state, ephemeral, and ephemeral memory strategies for efficient context retention in active sessions.

Learn to implement and apply short-term memory in agents using ADK, with step-by-step demos, hands-on exercises, and solution walkthroughs.

Explore using external APIs for real-time data, dynamic actions, and authenticating agents. Discover MCP, a protocol standardizing AI’s tool interoperability and safety.

Learn to integrate APIs securely using ADK and Google Cloud Secret Manager with hands-on demos and practical exercises.

Equip agents to search web for real-time, unstructured info. Ground responses in evidence using APIs, handle noise, and avoid hallucination for credible answers.

Build web search agents using ADK, integrate with Google Search for grounding, and apply practical skills through demos and hands-on exercises.

Equip agents to access and modify structured data by using SQL for interaction and vector databases for semantic tasks, ensuring seamless integration with private systems.

Learn to implement and practice database interaction using ADK and MCP Database Toolkit through demos, hands-on exercises, and guided solutions.

Discover Agentic RAG: Enhance RAG by enabling reflection, query reformulation, and intelligent adaption for nuanced answers. Master retrieval, reasoning, and retry loops.

Learn to build a RAG agent on Google Cloud using ADK and Vertex AI Search for querying custom unstructured data, including setup, search integration, and grounded response generation.

Explore long-term agent memory: understand semantic, episodic, and procedural memories. Learn storage strategies and best practices for personalized, coherent interactions.

Learn to implement long-term conversational memory in agents using ADK and Vertex AI Agent Engine, enabling context recall and continuity across sessions with persistent memory storage.

Agent Evaluation guides assessing an agent’s task completion, quality, tool use, and system metrics using response, step, or trajectory strategies to ensure reliable and efficient operations.

Learn to implement agent observability by configuring ADK with OpenTelemetry and Google Cloud Tracing to monitor, trace, and analyze agent interactions and performance.