This article provides a systematic guide for developers to select and apply architectural design patterns when building agentic AI systems. It emphasizes that failures in AI agents are often architectural rather than just prompting issues, suggesting that choosing the right pattern is essential for predictability, scalability, and debuggability. The roadmap covers foundational reasoning loops, self-correction mechanisms, external tool integration, task planning, and multi-agent coordination.
Key topics include:
* The necessity of design patterns to prevent unpredictable agent behavior
* ReAct (Reasoning and Acting) as a default starting point for adaptive tasks
* Reflection patterns for improving output quality through self-critique
* Tool Use as an architectural foundation for interacting with external systems
* Planning strategies like Plan-and-Execute and Adaptive Planning
* Multi-agent collaboration via specialized roles and orchestration topologies
* Production safety, evaluation criteria, and human-in-the-loop workflows
Key topics include:
* The necessity of design patterns to prevent unpredictable agent behavior
* ReAct (Reasoning and Acting) as a default starting point for adaptive tasks
* Reflection patterns for improving output quality through self-critique
* Tool Use as an architectural foundation for interacting with external systems
* Planning strategies like Plan-and-Execute and Adaptive Planning
* Multi-agent collaboration via specialized roles and orchestration topologies
* Production safety, evaluation criteria, and human-in-the-loop workflows
