Systems | Development | Analytics | API | Testing

Multi-Database API Integration for AI Systems | DreamFactory

APIs are transforming how AI interacts with enterprise data. Instead of directly connecting AI to databases like MySQL, PostgreSQL, or MongoDB - which can lead to security risks, schema complexities, and high maintenance - APIs act as a secure middle layer. This approach simplifies data access, reduces risks, and ensures seamless integration with multiple databases.

Multi-device AI session continuity: how cross-device conversation sync works

You start a research task on your laptop, the network drops during a meeting, and when you open your phone to continue, the conversation is gone – you re-prompt, get partial duplicate results, and lose 30 minutes of work. The delivery layer dropped it. That's one of the most consistent problems teams hit when building AI applications. It's particularly acute in customer support, where a session belongs to the conversation - not to any single device, connection, or participant.

EP 67 | The "Wobbly" Nature of AI: Governing an Unpredictable Technology

AI governance is the Achilles heel of most enterprises. As organizations accelerate AI adoption, boardrooms face urgent questions about cybersecurity, compliance, resilience, and regulatory risk. In this episode of The AI Forecast, Paul Muller meets with Shoshana Rosenberg, author of “Practical AI Governance: Building a Program for Oversight and Strategy,” and creator of the Prism AI Governance Framework, about how leaders can build adaptable AI governance programs that strengthen their resilience to this susceptibility.

Podcast Highlight: AI agents are your new team -- now what? #Cloudera #Short #tech #Fyp

We're witnessing the rise of the multi-sapien workplace with humans working alongside AI agents. Tune into The AI Forecast to hear by agentic AI needs to be managed like human teams. This conversation goes beyond technology; Tatyana also reflects on leadership and representation in tech, challenging assumptions about opportunity, and exhibiting why diverse ways of thinking are critical in an AI-driven world.

Turn test data into release insights with AI | SmartBear MCP for Zephyr

Testing teams need to know if they’re ready for a release. Getting answers within Jira, however, often means jumping between multiple screens and reports. In this demo, see how you can query your test data with SmartBear MCP for Zephyr to get insights directly from your testing system of record, so you can make faster, more informed release decisions. From within AI tools like Copilot, Claude, or VS Code, you’ll learn how you can.

The quiet crisis in software quality - and what autonomous testing changes

There’s a tension building inside most engineering organizations right now, and not many people are talking about it openly. AI has given development teams an extraordinary gift: the ability to build faster than ever before. Features that once took days can be prototyped in hours. Applications that required large teams can now be scaffolded by a handful of engineers with the right tools. By almost every measure of development velocity, we are living through a remarkable moment.

Designing MCP Servers for Observability

Observability is the key to understanding and improving MCP servers. These servers connect AI agents to tools, but without visibility, issues like slow responses, errors, or security risks can go undetected. Observability helps track how agents interact with tools, pinpoint failures, and optimize performance.

Everything we announced at our Agentic Quality Engineering Platform launch

Over 1,000 people around the world tuned in as Tricentis CEO Kevin Thompson and VP of AI David Colwell unveiled our new integrated platform, followed by a live demo from Enterprise Solution Architect Matt Serpone. From our headquarters in Austin, Texas, we unveiled a unified solution designed to help enterprises treat quality as a coordinated system rather than a collection of disconnected tools.