When I started building AI agents, I did what most developers do: I wrote a bunch of if-statements. If the user asks about pricing, call… Read More »Agentic Routing vs Rules-Based Logic: Why Your AI Agent Needs to Make Its Own Decisions
In today’s rapidly evolving technological landscape, the role of a data professional is undergoing a profound transformation. As businesses increasingly leverage AI and intelligent systems,… Read More »From Data Engineer to Data Lead: Guiding the Future of Intelligent Data
Enterprise AI needs more than prompt engineering. Context engineering—systematically architecting the information ecosystems that power intelligent systems—is the next evolution. Data engineers possess the exact skills needed to lead this transformation from experimental demos to production-ready AI applications.
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
To move beyond isolated AI, a new set of communication standards is emerging to create a collaborative “Internet of Agents.” This guide explains the key protocols—MCP for tool access, A2A for internet-scale teamwork, and ACP for internal agent clusters—and how they form the foundational stack for the next generation of intelligent systems. By understanding how these protocols work together, technical leaders can design robust, interconnected AI architectures for the future.