Systems | Development | Analytics | API | Testing

Artificial intelligence is no longer a futuristic concept, it’s a business necessity. The rise of generative AI (Gen AI) has transformed it from a buzzword into a defining force reshaping industries at every level. What was once viewed as an experimental technology is now a key driver of competitiveness, innovation, and efficiency across the global economy.

In today’s fast-moving vibe coding world, speed often wins. Developers spin up APIs, microservices, and pipelines in minutes. But speed comes with a cost: consistency, compliance, and long-term maintainability of APIs often lag behind. If you’ve ever looked back at a project and realized that naming conventions slipped, security best practices were inconsistently applied, or APIs drifted away from your organizational guidelines, you’re not alone.

Accurate forecasting is one of the hardest problems for analytics teams. Demand shifts, supply chain constraints, and external factors like weather or pricing often interact in ways that simple models cannot capture. With the release of multivariate time series forecasting in Qlik Predict, business analysts can now model how multiple variables evolve together over time, directly inside Qlik Cloud, without writing code.

The recent global IT outage experienced by a major cloud hyperscaler was a disruptive, real-world reminder that downtime and service disruptions are inevitable. The event impacted services across banking, retail, and healthcare, and served as a powerful warning that relying on any single provider, or even a single cloud region, creates a critical business vulnerability. This outage highlights the critical risk of a single-provider strategy, rather than an inherent problem with the cloud.

We’re excited to announce our Real-Time Context Engine, now available in Early Access. It’s a key part of Confluent Intelligence, our vision to bring real-time data directly to production AI systems through the power of Apache Kafka and Apache Flink.

When we first introduced Streaming Agents, we were solving a fundamental challenge: Every AI problem is a data problem. When data is missing, stale, or inaccessible, even the most advanced agents and LLMs fail to deliver. How do we build scalable agents that aren’t just powerful in isolation, but part of multi-agent systems that are event-driven, replayable, and grounded in accurate data?

The true power of data emerges when streaming, analytics, and artificial intelligence (AI) connect—transforming real-time streaming data into actionable intelligence. Yet bridging that gap has long been one of the most complex challenges in modern data architecture. Confluent makes it effortless to capture and process continuous streams of data, while Databricks empowers teams to analyze, govern, and apply AI through Unity Catalog.

The most innovative companies know that artificial intelligence (AI) problems are fundamentally data problems. To produce the right results, AI applications, ranging from real-time fraud detection to autonomous agents, must share a common foundation: trustworthy data in motion.

If you’re running Confluent Platform or our new offering, Confluent Private Cloud, on-premises, you have your reasons: data sovereignty, regulatory compliance, or maybe a phased cloud migration. Your on-prem Apache Kafka isn’t going anywhere. It’s a critical part of your infrastructure.