This article explains when to use sub-agents versus agents as tools in multi-agent systems built with the Agent Development Kit. It highlights key differences in how each handles tasks, context, and state, providing practical examples for better architectural decisions.

This article discusses two patterns for connecting agents to isolated execution environments called sandboxes. The first pattern runs the agent inside the sandbox, while the second keeps the agent on a local server and uses the sandbox as a tool. Each method has its own benefits and trade-offs regarding security, update speed, and separation of concerns.

This article shares insights on creating AI agents that actually work in production, emphasizing the importance of context, memory, and effective architecture. It outlines common pitfalls in agent development and provides strategies to avoid them, ensuring agents enhance human productivity rather than replace it.

This article clarifies the difference between workflows and agents in AI applications, emphasizing that not all models are autonomous decision-makers. It outlines when to use workflows, single agents with tools, or multi-agent systems based on task complexity and requirements. The author provides practical guidance for avoiding overengineering in AI solutions.

This article outlines seven essential principles for creating a production-grade agent architecture. It draws on the author's extensive experience in enterprise architecture and AI systems, focusing on practical considerations for deployment in regulated environments.

This article explains how to enhance agent performance by using filesystem structures and bash commands instead of complex custom tools. By organizing data as files, agents can efficiently retrieve and manage context, leading to improved outcomes and reduced costs.