Designing Cognitive Architectures: Agentic Workflow Patterns from Scratch

The rapid evolution of Large Language Models (LLMs) and Artificial Intelligence (AI) agents has sparked the development of sophisticated workflow patterns that enhance AI systems’ capabilities. This comprehensive technical guide explores advanced agentic workflow patterns, examining their technical intricacies, key features, variants, and interrelationships. From self-correcting mechanisms to dynamic task orchestration using Directed Acyclic Graphs (DAGs), we provide an in-depth analysis tailored for AI researchers and practitioners looking to harness these patterns in real-world applications.

This article serves as a compilation of 8 important agentic workflow patterns, exploring common, intuitive, and emerging techniques in AI workflows. The patterns discussed are agnostic of any particular foundational model, and while the author uses Gemini as a primary example, these patterns are easily transferable to other models. Additionally, all the patterns covered in this guide are accompanied by the author's GitHub repository for further exploration and implementation.