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.
Introduction: The Currency of AI is the Token In the rapidly expanding universe of generative artificial intelligence, a new economic discipline has emerged as a… Read More »The New Tokenomics: A Comprehensive Guide to the Economics of Large Language Models
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
The rise of Large Language Models (LLMs) has ushered in an era of AI-driven transformation, with AI agents at the forefront, promising to automate complex… Read More »Optimizing Agentic Workflows: The Power of Fine-Tuning and “Focused” LLMs
Distributed caching is a cornerstone of modern data architecture and system design, providing the performance, scalability, and cost efficiency needed for today’s high-demand applications. By understanding its principles, use cases, and design strategies, teams can harness its potential to build robust and efficient systems.