Category · 4 articles
AI Engineering & Trends
The practical side of building with AI. Covers agent frameworks, the Model Context Protocol, emerging standards, AI detection, physical AI, and engineering topics that shape how real products get built.
Learning outcome
What this collection helps you do
Understand emerging AI engineering tools and protocols well enough to assess where they fit, what they require, and which limitations matter.
You will learn to
- 01Evaluate agent frameworks and tool-integration protocols
- 02Separate durable engineering patterns from short-lived trends
- 03Identify operational, security, and evaluation requirements
How to Build a RAG Application with LangChain and Pinecone
Build a reproducible RAG application that ingests a document, stores its embeddings in Pinecone, tests retrieval, returns sources, and handles unsupported questions.
Mosaic AI Agent Framework: The Complete Guide for Building Production AI Agents on Databricks in 2026
Mosaic AI Agent Framework is Databricks' built-in platform for building, evaluating, and deploying enterprise AI agents. This research-backed guide covers every component from Vector Search and MLflow tracing to Agent Bricks, Unity Catalog governance, and MCP integration, with real enterprise examples.
Physical AI for ML Engineers: What's Actually Different About Training Models That Control Robots
If you train language or vision models and want to move into physical AI, this is the transition guide nobody wrote. What changes about your data pipeline, your training loop, your evaluation setup, and your mental model when your model's outputs move motors instead of tokens.
UCP vs ACP vs MCP: Which Shopify Protocol Actually Matters for Your Store in 2026
Three protocols are competing to define how AI agents shop for your customers. This plain-English guide breaks down UCP, ACP, and MCP for Shopify developers and agencies - what each one does, where they overlap, and what to actually build first.