Systems | Development | Analytics | API | Testing

Gen AI call center co-pilots can provide enterprises with operational visibility and insights while automating repetitive tasks, to improve the customer experience. In this session, we’ll show how a large health insurance provider implemented an agentic co-pilot designed scale across multiple call centers and environments. To dive deep into the architecture and see a demo of the co-pilot, you can watch the webinar this blog is based on.

At ThoughtSpot, we’re on a mission to empower every business user to become a data champion. Over the past year, I’ve witnessed firsthand how organizations across Australia and New Zealand are embracing this vision, transforming the way they work, make decisions, and serve their customers. Today, I’m excited to share some of the incredible momentum we’re seeing in the region and to celebrate the forward-thinking organizations leading the charge.

AI is everywhere. Depending on who you ask, it’s either making developers obsolete, or it’s just hype. But for those of us who’ve actually used AI tools in real-world engineering workflows, especially in complex distributed systems, the truth lies somewhere in between. At Multiplayer, we’ve spent the past few years exploring how AI can—and can’t—help solve two of the most persistent challenges in distributed systems: documentation and debugging.

In the fast-paced world of modern software development, performance is no longer just a backend concern—it's a critical driver of user satisfaction, infrastructure cost, and business growth. At NodeSource, we're excited to unveil a new feature in the N|Solid platform that takes Node.js application performance to a new level: N|Sentinel.

As cloud native applications grow, managing resource usage and controlling costs becomes increasingly complex. Choreo's Cost Optimizer addresses this challenge by providing AI-driven insights and actionable recommendations, enabling developers and organizations to streamline resource utilization and reduce unnecessary expenditures.

Today, we're excited to announce the general availability of AI Manager in Kong Konnect, the platform to manage all of your API, AI, and event connectivity across all modern digital applications and AI agents.

AI has been booming and has become a transformative force in several industries, software testing and QA are no different. With the evolution of technology and complexity of applications, automation and big-volume data analysis require extra assistance, and that’s where AI might come in handy. In QA, AI could help improve the efficiency of test management tasks, reducing manual effort.

As businesses increasingly harness the power of artificial intelligence (AI) and large language models (LLMs), a new challenge emerges: managing the deluge of AI requests flooding systems. This exponential growth in AI traffic creates what could be considered a gratifying predicament—high demand for your AI services—but also introduces complex challenges that must be addressed for sustainable operations.

Artificial Intelligence (AI) is revolutionizing industries worldwide, making it indispensable for modern businesses. However, this rapid growth brings a challenge—traditional testing methods are no longer sufficient to ensure the reliability and quality of complex, data-driven AI systems that are prone to bias. To succeed in 2025, organizations must adopt specialized AI automation testing strategies that validate performance and maintain consumer trust.

AI has evolved rapidly—from basic algorithms that suggested content to generative models that create it. Now, we're entering the AI agent era. AI agents refer to sophisticated AI systems that use reasoning and iterative problem solving to achieve specific goals. Instead of waiting for instructions, they adapt and take initiative. Agentic AI has transformative potential for enterprises.