Hands-on AI Agent development with Google ADK, Gemini models, and Vertex AI ,multi-agent systems
β±οΈ Length: 3.3 total hours
β 4.38/5 rating
π₯ 643 students
π March 2026 update
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- Course Overview
- Explore the revolutionary landscape of autonomous systems through the Google Agent Development Kit (ADK), a comprehensive framework designed to transition from standard conversational interfaces to sophisticated, goal-driven agents that can interact with the physical and digital world.
- This course provides a deep dive into the Gemini 1.5 model family, focusing on how to leverage the massive context windows and multimodal capabilities of Pro and Flash versions to build agents that possess superior reasoning and complex problem-solving skills.
- Learn the architectural foundations of Multi-Agent Orchestration, where you will design ecosystems of specialized agents that communicate, delegate tasks, and collaborate to solve enterprise-level challenges that are too broad for a single model to handle.
- Engage with a curriculum centered on Vertex AI, Googleβs premier machine learning platform, to understand the lifecycle of an agent from initial prompt engineering and tool definition to deployment and real-time performance monitoring in a cloud-native environment.
- Requirements / Prerequisites
- A solid command of Python Programming is required, as the course involves writing scripts to define agent behaviors, manage asynchronous API calls, and handle structured data formats like JSON for tool integration.
- Prospective students should have an active Google Cloud Platform (GCP) account with administrative access to Vertex AI services, as the hands-on labs require interacting with live cloud resources and API endpoints.
- Basic knowledge of Generative AI Fundamentals, including an understanding of how Large Language Models (LLMs) function, the importance of system instructions, and the general concepts of tokens and inference.
- Familiarity with Web Technologies and APIs, such as RESTful services and authentication protocols, will be beneficial when connecting your AI agents to external third-party tools and proprietary data sources.
- Skills Covered / Tools Used
- Mastery of the Vertex AI Agent Builder, utilizing its high-level orchestration layers to create agents that can perform search, data retrieval, and executive actions with high reliability and low latency.
- Advanced implementation of Function Calling and Tool Use, enabling your Gemini-powered agents to generate executable code snippets that trigger real-world actions like database queries, email dispatch, or API updates.
- Integration of Agentic Retrieval-Augmented Generation (RAG), moving beyond simple document lookup to allow agents to browse the web via Google Search or query internal vector databases for grounded, factual responses.
- Development of Stateful Agentic Workflows, where you will learn to manage long-term memory and context across multiple interaction turns, ensuring that agents maintain a coherent understanding of user intent and project history.
- Utilization of the Google Cloud SDK and associated Python libraries to programmatically control model parameters, manage deployment versions, and automate the testing of agent responses against predefined benchmarks.
- Techniques for Prompt Engineering for Agents, specifically focusing on Chain-of-Thought (CoT) and ReAct (Reason + Act) prompting strategies to improve the logical flow and accuracy of agent decision-making.
- Benefits / Outcomes
- Gain the professional capability to architect Production-Ready AI Agents that can be immediately integrated into corporate workflows to automate customer service, technical support, or internal data processing.
- Develop a competitive edge in the rapidly evolving AI job market by specializing in the Google Cloud AI Ecosystem, which is increasingly favored by enterprises for its security, scalability, and integration with the Gemini model series.
- Acquire the skills to build Scalable Multi-Agent Systems that can handle high-volume traffic and complex operations, significantly reducing the manual labor required for repetitive digital tasks and data synthesis.
- Understand how to implement Safety and Grounding Guardrails within your AI systems, ensuring that your agents remain compliant with organizational policies and avoid generating misleading or harmful content during interactions.
- Complete the course with a Robust Technical Portfolio featuring functional agentic projects, proving your ability to navigate the complexities of modern AI development from the ground up to full cloud deployment.
- PROS
- The content is Extremely Up-to-Date, featuring a March 2026 update that incorporates the latest features of the Google ADK and the most recent iterations of the Gemini model suite.
- A Highly Efficient Learning Path that condenses a vast amount of technical knowledge into just 3.3 hours, making it perfect for working professionals who need to master new tools quickly.
- Strong emphasis on Practical Application, moving quickly from theoretical concepts to hands-on implementation, which helps solidify learning through immediate trial and error in the GCP environment.
- Access to Modern Agentic Patterns, including multi-agent collaboration, which is the current “state-of-the-art” in AI engineering, moving beyond basic single-turn chatbot logic.
- CONS
- The course is Vendor-Specific, focusing exclusively on the Google Cloud platform and the Gemini ecosystem, which may require additional self-study for those looking to apply these concepts in AWS, Azure, or entirely open-source environments.
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