Agentic Workflows with Google ADK

Explore agentic AI workflows and get started with Google's Agent Development Kit by learning prerequisites, setup, and essential concepts.

Explores what defines a modern AI agent, its core components (Persona, Knowledge, Tools, Interaction), and the different types of agents based on their LLM interaction model.

Learn to implement agentic workflows using ADK by decomposing tasks and coordinating analyzer, coordinator, and validator agents for scalable, validated IT processes.

Design and visualize agentic workflows. Learn common agent types as building blocks for creating visual workflow diagrams.

Learn to design and implement agentic workflows in ADK using sequential, conditional, and parallel patterns, plus orchestration, error handling, and metrics for optimization.

Covers the practical aspects of translating agentic workflow models into Python code. Students learn to structure agent logic, define agent classes, and orchestrate their interactions.

Explore agentic workflow modeling with ADK and Vertex AI Gemini: manage states, visualize flows, handle errors, and analyze execution for robust multi-agent automation.

Introduces the Prompt Chaining pattern for breaking down complex tasks into a sequence of smaller, dependent steps. It covers strategies for task decomposition, validation, and context management.

Learn to implement multi-step agentic workflows with ADK, integrate Vertex AI Gemini LLM, use sequential and parallel patterns, and test agent performance.

Teaches the Routing pattern, which involves classifying incoming tasks and directing them to the most appropriate specialized agent or processing path.

Learn to build iterative agentic workflows using ADK and Vertex AI Gemini, implementing loop agents for automated refinement with generator and critic agents, escalation logic, and safe termination.

Introduces the Parallelization pattern for executing multiple agent tasks concurrently. It covers strategies for task decomposition (sharding, aspect-based) and result aggregation.

Learn to implement parallel agentic workflows using ADK and Vertex AI Gemini with fan-out/fan-in patterns for efficient concurrent task execution and automated result aggregation.

Focuses on the Evaluator-Optimizer pattern, an iterative process of generation, critique, and refinement to improve output quality. It emphasizes clear evaluation criteria and actionable feedback.

Learn to implement intelligent routing in ADK with Vertex AI Gemini, classifying content, selecting specialist agents, and building robust, rule-based workflows for multimodal data.

Introduces the advanced Orchestrator-Workers pattern, where a central agent dynamically plans, delegates, and synthesizes the work of multiple specialized worker agents.

Explore how to design and implement orchestrator-worker workflows using ADK and Vertex AI Gemini for scalable AI task management.