Author(s): Isuru Lakshan Ekanayaka

Originally published on Towards AI.

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In the rapidly evolving landscape of artificial intelligence, the past few years have witnessed unprecedented advancements in large language models (LLMs), diffusion models, and multimodal architectures. Among these breakthroughs, agentic workflows have emerged as a pivotal area driving significant progress. This comprehensive guide delves deep into the implementation of agentic workflows using the Llama Index library, exploring function calling, agent runners, Agentic Retrieval-Augmented Generation (RAG), and ReACT agents. Whether you’re an AI enthusiast, developer, or data scientist, this guide will equip you with the knowledge and practical insights to harness the full potential of agentic workflows.

Introduction to Agentic WorkflowsPrerequisitesSetting Up the EnvironmentFunction Calling with Llama IndexFunction Calling Agents and Agent RunnersAgentic Retrieval-Augmented Generation (RAG)ReACT Agents: Building a Search AssistantRoadmap and Next StepsConclusionAdditional Resources

Agentic workflows represent a paradigm where AI models operate with a degree of autonomy, enabling them to plan, reason, and execute tasks akin to human decision-making. This approach leverages various components such as function calling, planning mechanisms, and memory systems to create intelligent agents capable of complex interactions and problem-solving.

Early in 2023, Andrew W., the founder of Coursera and a notable figure in deep learning, emphasized… Read the full blog for free on Medium.

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Published via Towards AI