Agentic Architectural Patterns for Building Multi-Agent Systems
Proven design patterns and practices for GenAI, agents, RAG, LLMOps, and enterprise-scale AI systems
DACreated by Juan Pablo Bustos, Dr Ali Arsanjani
Explore proven design patterns and practices for building scalable multi-agent AI systems. Learn how to move from GenAI prototypes to robust, production-ready solutions using techniques like RAG, prompt engineering, and LLMOps. Real-world case studies and a clear maturity model guide your journey.
Packt | Jan 2026 | 574 min
What You Will Learn
You will follow a structured, design-patterns-first approach that combines technical guidance with practical examples. As you progress, you'll tackle real-world challenges, explore tradeoffs, and implement solutions using proven frameworks and tools. Each concept is reinforced with case studies and hands-on code examples to help you build confidence and expertise.
Key Features
- Apply agentic design patterns to solve coordination and reliability challenges
- Enhance AI systems with prompt engineering, RAG, and LLMOps best practices
- Build scalable, production-ready multi-agent architectures for enterprise needs
Target Audience
Ideal for AI developers, data scientists, and tech professionals ready to advance their GenAI skills. If you have a basic understanding of data and software concepts and want to build or scale agentic AI systems, you'll find practical guidance and advanced insights. Whether you're new to multi-agent architectures or seeking to deploy enterprise-grade solutions, you'll gain actionable strategies for success.
