Systems | Development | Analytics | API | Testing

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.

Data-driven organizations know that unlocking real-time analytics from streaming data isn’t just about collecting and transmitting events. It’s about getting high-quality, governed, and query-ready tables into the hands of analysts and business users while ensuring enterprise-grade security and compliance. Traditionally, moving data from Apache Kafka into analytic tables required complex ETL pipelines, manual data wrangling, and custom governance processes.

If you’re on a platform team running Apache Kafka, you know it’s rarely simple. You’re expected to keep it stable, performant, and secure while juggling requests from every direction. Supporting multiple teams and partners leads to operational complexity that never really goes away.

As a critical, stateful system, migrating Apache Kafka deployments is virtually always a complex engineering project where the most significant expenses are often hidden. Scoping and committing to a Kafka migration requires multiple layers of careful calculation involving infrastructure choices, data complexity, team expertise, and risk tolerance. Underestimating these variables leads to blown budgets and extended timelines.

The stakes for AI and data privacy are high. Risks in non-production environments are increasing. Regulatory bodies enact new privacy regulations each year. And concerns are rising as the AI/ML boom continues. Businesses like yours must be aware of key AI data privacy challenges. Read on to learn what these challenges are and how to address them.

‍ Every year, billions of dollars crisscross the globe, not through exotic trades but everyday remittances. In 2024, global remittance flows reached around USD 905 billion, growing at a CAGR of 4.6% over 2023. These flows now exceed foreign direct investment in many developing countries, underlining how critical remittances are for economies and families. ‍

Code stability and reliability can be a concern when building front-end applications. As web applications become more complex, web developers require a test framework that is simple yet incredibly powerful. This is where Jest testing comes in handy. Jest is a testing type designed and maintained by Facebook, which is already the ideal tool for most front-end programmers needing precise, fast, and robust test coverage.