Building Production-Ready MCP Agent Services: Eight Patterns That Matter
Model Context Protocol is the new standard for AI agent communication. Eight patterns for building production-ready MCP agent services
From Data Engineer to Data Lead: Guiding the Future of Intelligent Data
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
Building Production-Grade RAG Systems: An Architectural Deep Dive
Introduction Retrieval Augmented Generation has become a cornerstone of modern AI applications, but building a production-ready RAG system requires far more than connecting a vector… Read More »Building Production-Grade RAG Systems: An Architectural Deep Dive
From Reasoning to Orchestration: The Architectural Evolution of AI Agents
Why the industry is moving from reactive loops to planned orchestration, and what it means for your system design
Agentic Routing vs Rules-Based Logic: Why Your AI Agent Needs to Make Its Own Decisions
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
Context Engineering: The Data Engineer’s Guide to Building Intelligent AI Systems Beyond Prompt Engineering
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.
Building Scalable AI Systems: The Supervisor-Worker Architecture That Actually Works
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.