Mastering Production-Grade Agentic AI: Architect, Build, and Scale Autonomous Systems using Google ADK
β±οΈ Length: 6.7 total hours
β 4.59/5 rating
π₯ 367 students
π February 2026 update
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- Course Overview
- This curriculum is meticulously designed to transform software engineers into AI architects capable of building Production-Grade Agentic AI systems that go beyond simple chat interfaces.
- The course provides a deep dive into the Google Agent Development Kit (ADK), exploring how it streamlines the lifecycle of autonomous agents from initial conceptualization to final deployment.
- Participants will explore the revolutionary Model Context Protocol (MCP), understanding how it creates a standardized bridge between Large Language Models and various external data sources or tools.
- The syllabus focuses on the shift from Prompt Engineering to Agentic Orchestration, emphasizing the design of systems that can reason, plan, and execute multi-step tasks with minimal human intervention.
- You will examine the internal mechanics of Google Cloudβs AI Ecosystem, learning how to leverage the specialized infrastructure required for low-latency agentic responses and high-throughput data processing.
- The training covers the implementation of Self-Correction Loops and Reflexive Reasoning patterns, which are essential for ensuring that autonomous agents remain accurate and reliable in enterprise environments.
- Students will gain insights into the Architectural Nuances of stateful versus stateless agents, determining when to maintain context across long-running autonomous workflows.
- The course explores the integration of Multi-Agent Systems (MAS), where different specialized agents collaborate through a unified MCP-based communication layer to solve complex, high-level business objectives.
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- Requirements / Prerequisites
- A foundational mastery of Python Programming is essential, specifically focusing on asynchronous execution (async/await) and complex data structure manipulation.
- Intermediate knowledge of Cloud Infrastructure, preferably Google Cloud Platform (GCP), including an understanding of IAM roles, service accounts, and API authentication.
- Familiarity with RESTful API Design and the principles of JSON-RPC, which are foundational for configuring and extending MCP servers.
- A baseline understanding of Large Language Model (LLM) Fundamentals, such as tokens, context windows, and the difference between discriminative and generative AI.
- Basic experience with Containerization Tools like Docker, which are used to package and deploy MCP servers and agentic environments consistently across different platforms.
- An understanding of Development Environments, including proficiency in using VS Code or similar IDEs and managing virtual environments for dependency isolation.
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- Skills Covered / Tools Used
- Google ADK (Agent Development Kit): Mastering the framework for defining agent personas, toolsets, and interaction boundaries.
- MCP Server Implementation: Learning to build and deploy Model Context Protocol servers to expose databases, local files, and web services to AI agents.
- Vertex AI Orchestration: Utilizing Googleβs enterprise AI platform to manage model endpoints and monitor agent performance at scale.
- Gemini 1.5 & 2.0 Integration: Deep-diving into the specific capabilities of Googleβs latest models, including native multimodal processing and long-context reasoning.
- Agentic Tool-Use: Designing robust functions and schema definitions that allow agents to interact with third-party SaaS platforms and internal legacy systems.
- State Management & Memory: Implementing persistent storage solutions for agent memory, allowing for continuity across disconnected user sessions.
- Observability Frameworks: Using specialized logging and monitoring tools to trace agentic decision-making paths and identify “hallucination” bottlenecks.
- Security Protocols: Building “Human-in-the-Loop” (HITL) checkpoints and rigorous input/output sanitization to prevent prompt injection and unauthorized actions.
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- Benefits / Outcomes
- The ability to architect End-to-End Autonomous Workflows that can independently handle customer support, data analysis, or software development tasks.
- A significant competitive advantage in the AI Job Market, as mastery of MCP and Google ADK positions you at the forefront of the 2026 AI engineering landscape.
- The capacity to Reduce Operational Latency by optimizing tool-calling sequences and streamlining the way agents interact with enterprise data.
- Acquiring the expertise to transition from Proof-of-Concept (POC) agents to scalable, production-ready systems that meet enterprise security and reliability standards.
- Empowerment to build Scalable Knowledge Retrievers that use MCP servers to provide agents with real-time, ground-truth data, eliminating common model inaccuracies.
- Understanding how to implement Cost-Effective AI Architectures by intelligently routing tasks between different model sizes and optimizing context window usage.
- Professional confidence in Debugging Agentic Reasoning, utilizing advanced tracing techniques to understand why an agent took a specific set of actions.
- PROS
- Provides early-adopter access to the Model Context Protocol ecosystem, which is rapidly becoming the industry standard for AI interoperability.
- Focuses on Enterprise-Grade Security, ensuring that the agents you build are safe for deployment in regulated industries like finance and healthcare.
- Includes Practical Hands-On Labs that simulate real-world scenarios, moving beyond theoretical slides to actual code implementation.
- CONS
- The Rapid Evolution of the Google ADK and MCP specifications means that some technical syntax may undergo significant changes shortly after the course update.
Learning Tracks: English,IT & Software,Other IT & Software
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