Agentic Architectural Patterns for Multi-Agent Systems

Advance your expertise in agentic AI by exploring self-improving systems, strategic implementation roadmaps, and practical frameworks for deploying production-grade multi-agent solutions. Learn from detailed use cases and comparative analyses of leading agent frameworks. This course guides learners through advanced adaptation techniques, operational frameworks for self-improving agents, and strategic roadmaps for implementing agentic patterns at scale. Participants will examine real-world use cases in loan processing, compare leading agent frameworks such as CrewAI and LangGraph, and gain insights into responsible AI practices and production readiness. The course concludes with actionable strategies for achieving higher levels of agentic maturity and operational excellence. Combining in-depth case studies, comparative framework analyses, and strategic guidance, the course empowers learners to design, implement, and iterate on advanced agentic AI systems. Emphasis is placed on practical application, maturity assessment, and continuous improvement. This course is part three of a three-course Specialization designed to build a complete and cohesive understanding of the subject. While it offers valuable skills on its own, you'll gain the most benefit by progressing through all three courses as a structured learning journey. This course is based on Agentic Architectural Patterns for Building Multi-Agent Systems, by Dr. Ali Arsanjani and Juan Pablo Bustos. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.

Explore the foundational concepts, architectures, and adaptation strategies for building agentic AI systems in enterprise environments. Learn how to select, deploy, and adapt large language models to create robust, agent-ready solutions. This course introduces the landscape of GenAI in the enterprise, focusing on the essential architectural features and challenges of agentic AI systems. Learners will gain practical knowledge on selecting and deploying large language models, understanding adaptation techniques such as retrieval-augmented generation (RAG) and fine-tuning, and designing hierarchical agentic architectures for business process automation. By the end of the course, participants will be equipped to make informed decisions about model selection, adaptation, and deployment for agentic AI applications. The course blends conceptual overviews with real-world case studies and technical guidance, providing a structured pathway from foundational principles to practical implementation. Learners will engage with frameworks, tradeoff analyses, and step-by-step examples to build a strong foundation in agentic AI. This course is part one of a three-course Specialization designed to build a complete and cohesive understanding of the subject. While it offers valuable skills on its own, you'll gain the most benefit by progressing through all three courses as a structured learning journey. This course is based on Agentic Architectural Patterns for Building Multi-Agent Systems, by Dr. Ali Arsanjani and Juan Pablo Bustos. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.