What's the difference between Agentic AI and LLMs?
In this clip, Angie Jones explains how agentic AI goes beyond language prediction to actually take action — and what that means for the future of software development and testing.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
Is Data Integration the Real Engine Behind Effective AI Agents? #aiagents
Jay Mishra, our Chief Product and Technology Officer, explains why quality data is the true driving force behind successful AI agents. He also shares how Astera AI Agent Builder seamlessly connects to both internal and external data sources, ensuring that your AI agents are data-driven and ready to deliver powerful results.
The Difference Between Agentic AI and GenAI
Not all AI is agentic. Steffen Hoellinger, Airy’s CEO, breaks down the difference between agentic AI and generative AI, highlighting how AI agents utilize streaming data to reason and act.
Why Data Teams Are Best-Positioned For Agentic AI Success With Data Integration and MCPs
Building AI agents is the first step, and it’s positive to see enterprises exploring this avenue. But it’s only the first step. For true enterprise value, these agents must seamlessly connect to your data ecosystem through robust integration, standardized protocols, and be guided by knowledgeable data teams. The need to give AI agents access to data and connect them to the necessary tools and functions has led to the creation of the Model Context Protocol (MCP).
A Vision for the Future: Qlik's New Agentic AI Experience
The future of data and analytics will be nothing like the experience we're used to today. We are at the beginning of a transformation that will fundamentally reshape how businesses use data, make decisions, and create value. At the center of this revolution is Agentic AI. Agentic AI fundamentally changes the way we work with data – moving from passive, reactive AI systems to autonomous, goal-oriented agents capable of reasoning, planning, and executing complex tasks across diverse data landscapes.
MCPs, Agents and the Future of Software Testing with Angie Jones
In this LIVE episode of Test Case Scenario, host Jason Baum, along with co-hosts Marcus Merrell and Evelyn Coleman, engages in a compelling conversation with Angie Jones, Global Vice President of Developer Relations, Block, Inc. They delve into the transformative impact of agentic AI and Model Context Protocols (MCPs) on software development and testing.
EP 23 | From Poker Tables to Boardrooms: AI, Risk, and Business Communication with David Daneshgar
In high-stakes environments like professional poker and startup entrepreneurship, precision, timing, and strategy are everything. And nobody knows that better than David Daneshgar. In this episode of The AI Forecast, we’re joined by David Daneshgar, a World Series of Poker champion and now Co-founder and CEO of Whippy, a company using AI to transform how businesses communicate with their customers.
From silos to AI success: 4 global enterprises on Fivetran + Snowflake
Global enterprises HubSpot, Saks, DocuSign, and Oldcastle Infrastructure modernized their data infrastructure on Fivetran and Snowflake, achieving AI-driven innovation, scalability, and millions in ROI.
What is MCP? Diving Deep into the Future of Remote AI Context
The hype for Anthropic’s Model Context Protocol (MCP) has reached a boiling point. Everyone (including Kong) is releasing something around MCP to ensure they aren't seen as falling behind in the ever-changing AI landscape. However, in this mad dash, there remains confusion around MCP and what this standard actually enables. Some see MCP as a total game-changer, and some see it as little more than a thin and unnecessary wrapper. As usual, the truth lies somewhere in between.
AI ETL Tools: Revolutionizing Data Engineering
In 2025, the integration of Artificial Intelligence (AI) into Extract, Transform, Load (ETL) processes is transforming the data engineering landscape. Traditional ETL workflows are evolving from rigid, manually scripted pipelines into intelligent, adaptable systems powered by AI. These AI-driven ETL tools enable companies to handle increasing data complexity, schema drift, and real-time transformation demands without massive engineering overhead.