From Single-Agent to Multi-Agent: Why and How
As a technical founder, scaling your system architecture is a critical decision. Single-agent systems, while simple, face limitations such as bottlenecks, lack of specialization, and scalability issues. Transitioning to multi-agent systems distributes tasks across specialized agents, enabling faster, modular, and scalable solutions. Multi-agent architectures like Network and Supervisor have distinct strengths and weaknesses. Networks are flexible but risk inefficiencies due to chaotic task routing. Supervisors, on the other hand, improve coordination and debugging but rely on a central controller. Choosing the right architecture depends on your goals. For small systems, networks can work with careful management. Larger systems benefit more from supervisors or hierarchical setups. Tools like Langraph can help prototype and refine your architecture efficiently. By adopting multi-agent systems, you can create AI tools that are smarter, faster, and scalable to meet growing demands.
How to Build a Travel AI MVP with RAG and Function Calls: A Founder’s Guide to Fast Prototyping
Building a Travel AI MVP can be challenging, but leveraging Retrieval-Augmented Generation (RAG) and function calls makes it efficient and scalable. This guide explores how these technologies work together, compares frameworks like LangChain, Rasa, and LlamaIndex, and explains why LangChain is the best choice for creating a dynamic and user-focused travel AI. Learn the key steps and insights to accelerate your AI-powered travel solution as a founder and software engineer.