Google Agentic AI Engineer

Explore agentic AI concepts using Gemini models and Google Cloud Platform, covering prerequisites, environment setup, and key features for effective LLM reasoning.

Explains the theory of using roles or personas to control the tone, style, and expertise of an LLM's output.

Learn to create expert business personas for Vertex AI Gemini, guiding the model with role-based prompts to produce domain-specific, high-quality, specialized AI responses.

Explains the conceptual frameworks for Chain-of-Thought (CoT) for guided reasoning and ReAct (Reason+Act) for enabling agents to plan and take actions.

Learn to build iterative, reasoning agents with Chain-of-Thought and ReACT prompting using Vertex AI Gemini to solve complex, multi-step business problems systematically.

Explains the theory of systematically refining prompt instructions by modifying components like Role, Task, Context, Examples, and Output Format.

Learn to refine and optimize AI prompts using Vertex AI Gemini, applying systematic quality metrics like clarity, specificity, completeness, and structure.

Explains the conceptual framework for building multi-step AI workflows by linking the output of one prompt to the input of the next, and the importance of validation.

Learn to implement prompt chaining with Vertex AI Gemini for agentic reasoning: build sequential workflows, manage context, handle errors, and ensure quality in multi-step tasks.

Explains the conceptual framework for building self-improving systems where an agent uses feedback from its own actions to iteratively refine its output.

Learn to implement automated feedback loops for code generation using Vertex AI Gemini, covering self-validation, intelligent retries, quality gates, and production monitoring.

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.

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.

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

Explain the core components of multi-agent systems and how to design their high-level architecture.

Design multi-agent systems using the Orchestrator–Worker pattern and formalize agent capabilities with A2A Agent Cards to enable standardized service communication.

Develop a multi-agent system by coding the designed architecture and connecting agents with well-defined interfaces.

Implement multi-agent architectures with ADK by creating specialized sub-agents with focused tools, orchestrators that route intent-based tasks, and structured delegation workflows.

Apply orchestration techniques to coordinate multiple agent actions and achieve complex workflows.

Build complex business processes using SequentialAgent for ordered pipelines and ParallelAgent for concurrent execution, optimizing workflows through intelligent task dependency management.

Configure routing mechanisms to manage data flow among agents in multi-agent systems.

Implement deterministic business rule routing with CustomAgent to inspect events and programmatically enforce compliance, discounts, and strict logic without LLM uncertainty.

Evaluate methods for tracking and updating agent state across multi-turn interactions.

Master explicit session state management using ToolContext and InvocationContext to persist complex data across multi-turn conversations with cloud-based Vertex AI Agent Engine.

Develop a coordinated multi-agent system that synchronizes states for coherent task execution.

Build distributed multi-agent microservices using RemoteA2aAgent for A2A communication, Agent Cards for service discovery, and MCP Database Toolbox for shared MySQL state across independent services.

Extend RAG to multiple cooperating agents, each specialized in certain retrieval tasks.

Implement enterprise multi-agent RAG systems that combine Vertex AI Search for unstructured docs with SQL databases for structured data, enabling hybrid knowledge sharing across agents.