Systems | Development | Analytics | API | Testing

Agentic AI Integration: Why Gartner's "Context Mesh" Changes Everything

Gartner just published research that should be required reading for every platform and infrastructure leader building for the agentic era. The report, "How to Enable Agentic AI via API-Based Integration," makes a stark claim: incrementally reworking existing APIs and connector-based integrations for AI agents is no longer sufficient.

Pre-Training vs Fine-Tuning vs RAG: Which AI Approach Fits Your Business in 2026?

Every organization today is racing to embed AI into its core, yet the real question isn’t which model to choose, but how to build an AI capability that truly aligns with your business goals. Should you invest months in training a proprietary model to gain full control and differentiation? Or would adapting a pre-trained model strike a better balance between performance and time-to-market?

What is an MCP for Kafka with Tun Shwe

AI agents are only as good as the data they can access. In this video, we explore the Model Context Protocol (MCP) and how it creates a bridge between AI models and Apache Kafka. Learn how MCP allows AI agents to securely produce, consume, and manage Kafka topics in real-time—transforming your event streams into actionable context for LLMs.

Snowflake Ventures On Accelerating Startups In The AI Data Cloud

In this interview, Stefan Williams, VP of Corporate Development, shares Snowflake Ventures’ top priorities and why the pace of innovation across the AI Data Cloud matters for startups. Learn how native platform capabilities, including Cortex Search, Cortex Analyst, and the Cortex REST API, help reduce complexity and unlock faster innovation. The Snowflake ecosystem is where startups gain secure, trusted access to data and the opportunity to work with some of the world’s largest customers.

How to Use GenAI to Build Load Test Scripts in Apache JMeter | Sandeep Garg

This demonstration shall suggest those baby steps that should encourage most of us (the testers) to inculcate the habit of exploring* GenAI and LLMs. The exploration shall help understand why, what, where, when and how we should use these rapidly moving technologies to bring efficiency and better thinking in our day-to-day testing work.

AI Maturity and Adoption Across U.S. States in 2026

AI isn’t something we’re waiting for anymore. It’s already here. Every time you check directions, talk to your phone instead of typing, or unlock your device with your face, AI is doing the work in the background. The same shift is happening inside organizations. Companies and government agencies are using AI to answer questions faster, cut down manual tasks, support their teams, and improve how they serve people. But the real question is, Are U.S.

5 tips to build a durable career in the age of AI

“We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.” That’s Amara’s Law, a principle coined by futurist Roy Amara. It explains how emerging technologies, like the early internet, are often overhyped at first, followed by a shift toward recognizing their value and integrating them over time. This thinking is a lot like what we’re seeing today with agentic AI.

Making AI Work in Real Teams. Operationalizing AI Explained | Melissa Tondi

Let's talk about the real-world journey of operationalizing AI—what it looks like behind the scenes when you’re scaling solutions, building support systems, and doing it all with a lean team. There are plenty of brilliant AI experts—folks doing deep work, research, experimentation, and implementation. This session complements and focuses on how to operationalize, centralize and scale your team, organization and company!