Systems | Development | Analytics | API | Testing

As a dad of two toddlers with very particular tastes—one constantly wants treats for dinner and the other refuses anything that isn’t beige—I view dinnertime at my house as a nightly episode of “Chopped: Toddler Edition.” Add early bedtimes and the need to avoid meltdowns (theirs and mine), and the meal becomes less about gourmet aspirations and more about survival. The goal? Walk away with everyone fed, happy, and preferably not covered in food.

Artificial intelligence (AI) agents are everywhere. Salesforce has Agentforce, Google launched Agentspace, and Snowflake recently announced Cortex Agents. But there’s a problem: They don’t talk to each other. Your customer relationship management (CRM) agent doesn’t know what insights your data warehouse agent has. Your knowledge retrieval agent operates in isolation. Instead of having a connected AI ecosystem, we’re repeating history and creating AI silos.

The divide between operational and analytical systems has long resulted in data inconsistencies, unreliability, and redundancies. Without a single, unified source of truth, teams interpret information in their own ways—often after the fact. This can lead to downstream data discrepancies, issues, and distrust. Meanwhile, changes to upstream data structures create ripple effects, breaking downstream systems and requiring manual intervention to fix issues.

Supply chain disruption. Pandemics and medical crises. Climate change. Events that were once “black swans” are now near-everyday occurrences for citizens worldwide—and governments are facing growing pressure to provide effective public services in response. In the past, the ability to access, process, and apply data to make informed decisions about complex issues was crucial to creating effective public services.

Confluent has been instrumental in enabling customers from various industries to develop real-time stream processing solutions using Apache Kafka. While many of these use cases demand low-latency and real-time processing, stream processing is also increasingly being utilized for ingesting logging and telemetry data. This type of data typically features a high ingest rate, but allows for a higher tolerance for end-to-end processing time.

Model Context Protocol (MCP), introduced by Anthropic, is a new standard that simplifies artificial intelligence (AI) integrations by providing a secure, consistent way to connect AI agents with external tools and data sources. When we saw MCP’s potential, we immediately started exploring how we could bring real-time data streaming into the mix. With our long history of supporting open source and open standards, building an MCP server was a natural fit.