Systems | Development | Analytics | API | Testing

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.

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?

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.

At Confluent, we know that our platform must provide your business with resilience for your mission-critical applications, and we take that responsibility very seriously. Any unplanned outages can result in lost revenue, reputation damage, or fines. As incidents inevitably happen, your organization needs to know how to maximize your availability with our products.

Thanks to ever-increasing adoption technologies like Apache Kafka and Apache Flink, the continuous movement and streaming of real-time data has transformed how modern businesses operate… but is the cost of data streaming worth it? From powering personalized recommendations to enabling instant fraud detection, streaming is often seen as synonymous with innovation and competitive advantage. But like any investment, the cost-benefit equation has to make sense.

Traditionally, compliance teams have had to rely on batch exports for their audit logs, a method that, while functional, is proving to be woefully inadequate in today's fast-paced digital landscape. The truth is, waiting hours, or even days, for batch exports of your audit data leaves your organization vulnerable.

Migrating from self-managed Apache Kafka connectors to fully managed connectors has been a persistent challenge for data teams working on Confluent Cloud. While Confluent-managed connectors deliver enterprise-grade features, seamless upgrades, and comprehensive support that add up to significant development and operations cost savings, the journey to get there often feels daunting and opaque.

I’m a Data Streaming Engineer and a developer advocate, which means I spend a lot of time thinking about the day-to-day experience of building applications with data streaming and stream processing. I muse about a world of data in motion where entire organizations have the governance needed to manage, discover, and understand the complex relationships between data streams.

In the evolving landscape of agentic systems, Confluent and Google Cloud together emerge as critical enablers, providing the real-time infrastructure that underpins efficient, reliable, and intelligent data flow. This powerful synergy addresses key challenges in agent-to-agent (A2A) communication, interaction with external resources, and the overall stability and observability of complex multi-agent environments.

In our previous blog post, we introduced two ways that Confluent Cloud can integrate with AWS Lambda. One option is using Lambda’s Event Source Mapping (ESM) for Apache Kafka, wherein Lambda creates a consumer group, consumes records off the provided topic, and triggers the Lambda function. The record is polled by the ESM, and the consumed record subsequently acts as the event data provided to (and processed by) the Lambda function.