Systems | Development | Analytics | API | Testing

Connect AI Agents with MCP | Do It Better with FME & Snowflake

See how AI agents use Model Context Protocol (MCP) with FME to connect complex enterprise and spatial data to Snowflake. In this episode of Do It Better with Snowflake, Safe Software CEO Don Murray demonstrates how FME extends Snowflake Cortex AI Agents with FME workflows for data integration, transformation, and spatial analysis. See how Snowflake + FME can help you: Connect complex data: including GIS, CAD, 3D, LiDAR, BIM, ERP, and unstructured data.

Claude + MCPs Isn't a Semantic Layer: What Breaks When You Analyze Your Business Without One

Ask Claude for your average sales cycle today. Ask again next Tuesday. Same prompt, same MCPs, same playbook. There is a real chance the two numbers do not match. The reason is not exciting, and it costs you decisions. If you have wired Claude to your CRM, product, and finance tools through MCPs and given it a detailed playbook, you are already ahead of most companies asking whether AI can analyze the business. The gap between that and running the company off the answers is real, though.

How to fix API contract drift with AI agent skills | SmartBear Swagger Contract Testing

Your AI coding agent can already write code. Can it verify a contract, run provider checks, and tell you whether it's safe to deploy? This video shows you how to set up SmartBear Swagger Contract Testing drift AI Agent Skills, so your agent can spot API drift and write the missing tests for you, without needing the context multiple times.

Snow Report: What's Happening At Snowflake in August

The August Snow Report is LIVE Two GA launches for CoCo, warehouse tuning that handles itself, and World Tour in 23 cities. What's new CoCo Desktop is GA A native IDE inside your governed Snowflake environment, so it knows your data models and access policies from day one. Up to 2X faster on complex tasks with 51% fewer tokens than third-party assistants. Cloud Agents are GA Run agentic workflows from your browser in Snowsight. Start with a prompt, close your session, and the agent keeps working on Snowflake's managed infrastructure.

SQL-Shaped Intent: The Engineering Behind AgentQL

Our CEO recently wrote reaffirming an architectural decision ThoughtSpot made when LLMs first emerged: we do not use LLMs to directly generate SQL. My team has spent the better part of a year building AgentQL: a capability that doubles down on our decision. So let me explain what we actually built, why it doesn't just honor that architectural decision but depends on it, and the engineering choices underneath.

Ep 85 | Enterprise AI Success: What Separates Results from Expensive Experiments

Most enterprise AI use cases still aren't delivering measurable value. So what separates the projects that work from the ones that quietly disappear? For Mark Ritcey, the answer comes down to disciplined execution. AI programs need a clear business problem and an organization prepared for how the technology changes the way work gets done. In this episode of The AI Forecast, Paul Muller sits down with Mark Ritcey, Vice President of AI and Automation Delivery at Latentbridge and lecturer on AI and machine learning, to examine the decisions that shape enterprise AI success.