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The world is leaping towards building products with agentic architecture; those powered by autonomous, goal-driven AI agents. These systems won’t just respond; they will reason, plan, and act independently. But with such autonomy comes complexity. Traditional engineering and product management models are no longer sufficient. Enter Context-Driven AI Engineering,  an approach that embeds the surrounding situational, user, and system-level context into every stage of AI product development. For product managers (PMs) working at the intersection of data, design, and delivery, this isn't just a methodology, it's a necessity. Why Context is Critical in AI Engineering Autonomous agents interact with complex environments: users, APIs, documents, dynamic tasks, and even other agents. Without context-awareness, they risk becoming brittle, hallucinating, or acting inappropriately. Context isn't just data, it's relevance . It includes: User context : Who is interacting, with wha...

  Introduction The Model Context Protocol (MCP) is an open standard introduced by Anthropic with the goal to standardize how AI applications (chatbots, IDE assistants, or custom agents) connect with external tools, data sources, and systems. Think of it like USB for AI integrations. In this comprehensive guide, we'll build a real-world MCP server from scratch using Python. What is MCP? The Model Context Protocol is an open standard that enables developers to build secure, two-way connections between their data sources and AI-powered tools. MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. MCP Architecture Overview ┌─────────────────┐    MCP Protocol    ┌─────────────────┐ │   MCP Client    │◄──────────────────►│   MCP Server    │ │  (AI Assistant) │                    │ (Your ...