Reading
This project demonstrates four agent workflow patterns based on large language models (LLMs), covering tool usage, multi-agent planning, interactive web applications, and multi-agent customer service systems, providing practical references for AI application development.
Section 01
This project is maintained by williamrsandoval-cyber and was published on GitHub (2026-05-23). It demonstrates four agent workflow patterns based on large language models (LLMs), covering tool usage, multi-agent planning, interactive web applications, and multi-agent customer service systems with guardrails. Implemented using OpenAI and Groq APIs, it provides practical references for AI application development.
Section 02
With the rapid evolution of LLM capabilities, AI applications are shifting from simple Q&A to complex agent forms. Agents can call tools, plan tasks, and collaborate to complete complex tasks. This project from Arizona State University's Master of Business in Artificial Intelligence program systematically explores four typical agent workflow patterns, providing reference implementations for learners and developers.
Section 03
The project implements four agent workflows with different architectures:
Section 04
Core: Enable LLMs to call external tools to expand capabilities
Core: Decompose complex tasks into subtasks and complete them through multi-LLM collaboration
Section 05
Core: Encapsulate agent capabilities into a user-friendly web interface
Core: Multi-agent collaboration to complete customer service tasks
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Section 08
The project covers core scenarios from tool usage to multi-agent collaboration, and from prototypes to production-level systems, making it an important resource for learning agent technology.
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