Overview

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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.

Syllabus

GenAI in the Enterprise: Landscape, Maturity, and Agent Focus
- This module introduces the strategic frameworks and key concepts behind deploying generative AI (GenAI) in enterprise settings, with a special focus on agentic AI systems. Learners will explore business applications, architectural features, and the challenges of moving from prototypes to production-grade solutions. By the end, you'll understand how GenAI is transforming organizations and what it takes to build scalable, modular AI agents.
Agent-Ready LLMs: Selection, Deployment, and Adaptation
- This module guides learners through the process of selecting, deploying, and optimizing large language models (LLMs) for agentic AI systems. Key topics include model selection criteria, technical specifications like context window and tool use, deployment strategies, performance optimization, and security considerations. Learners will gain practical insights into managing LLMs as the cognitive core of agent-based architectures.
The Spectrum of LLM Adaptation for Agents: RAG to Fine-tuning
- This module explores the range of techniques for adapting large language models (LLMs) to specialized agent roles, focusing on Retrieval-Augmented Generation (RAG), in-context learning (ICL), and various fine-tuning strategies. Learners will examine hierarchical agent architectures, real-world business scenarios, and best practices for grounding model outputs to ensure reliability and compliance. By the end, you'll understand how to select and implement the right adaptation approach for different enterprise agent needs.
Agentic AI Architecture: Components and Interactions
- This module delves into the architecture of agentic AI systems, examining how autonomous agents interact with their environments, utilize data stores, and coordinate to achieve complex goals. Learners will explore real-world examples, such as a travel planning agent, and address key technical considerations for building robust, context-aware AI solutions.