Your agent workflows can become faster, smarter, and more reliable. In this hands-on course, you'll learn the Agno framework, an AI agent toolkit that helps developers design agents, orchestrate multi-agent teams, integrate knowledge systems, manage memory, and deploy production-grade agentic AI directly in your development environment.

Whether you want to reduce manual workflow coordination, improve system reliability, or understand how AI agents support modern software architecture, this course teaches you how to use Agno effectively and responsibly. You'll begin by exploring how Agno works, including its architecture, execution model, context handling, and multi-agent orchestration capabilities. Then, you'll move through practical exercises—from building your first single agent and integrating custom tools to implementing knowledge retrieval, managing persistent memory, orchestrating multi-agent teams, debugging agent behavior, and applying Agno in production workflows. By the end of this course, you will be able to: 1. Define Agno's core capabilities and explain how architecture, context, prompts, tool integration, and multi-agent orchestration support AI-assisted automation. 2. Use Agno's agent APIs, tool integration, and structured outputs to build, explain, debug, and refactor intelligent agentic workflows efficiently. 3. Write effective tool definitions and prompts that guide agents toward accurate, secure, and maintainable code and system outputs. 4. Review and validate agent-generated decisions using logging, testing, monitoring, and human-in-the-loop decision-making. 5. Apply Agno across knowledge retrieval, multi-agent coordination, system observability, and full-stack agentic application development. This course is designed for software developers, backend engineers, fullstack developers, AI/ML practitioners, DevOps professionals, early-career developers, and learners who want to understand how Agno can support real agentic AI workflows. If you are new to Agno or new to multi-agent systems, this course provides a practical starting point. Learners should have basic experience writing code in Python. Familiarity with APIs, command-line usage, and LLM concepts is helpful, along with a willingness to practice through hands-on coding tasks. Enroll now and learn how to build, test, debug, and deploy intelligent agents with Agno. Start with the fundamentals, practice with real agentic workflows, and build confidence using AI agents as part of modern software development.