The supervisor-worker architecture moves beyond monolithic AI by using a central “supervisor” to manage a team of specialized “worker” agents. This modular approach enables scalable, agile, and more reliable AI systems by delegating tasks to experts, mirroring an efficient human organization.
RAG and MCP aren’t rivals—they’re complementary. RAG helps AI know by retrieving knowledge; MCP helps it act by executing tasks through standardized tool access. The future isn’t choosing one, but combining both to build AI agents that understand and get things done. Stop comparing—start architecting for action.
The rapid evolution of AI, particularly the rise of sophisticated AI agents, demands new approaches to how these agents understand and interact with the world.… Read More »Model Context Protocol (MCP): Fueling the Next Generation of AI Agents
Machine learning (ML) is a branch of artificial intelligence (AI) that enables systems to learn patterns from data and make predictions or decisions without being… Read More »Core Machine Learning Concepts Explained
Search algorithms are fundamental to computer science, used for finding elements in data structures efficiently. In this tutorial, we’ll cover essential search algorithms, from basic to advanced, using Python with challenge problems included.
An introduction to python data structures as tools to organize and manage your data so you can work with it more efficiently