On September 15, 2026, Aiven, Confluent, Redpanda, StreamNative, and Ververica published an open definition of Streamhouse, a data architecture that keeps the current state of a business continuously available to production applications, analytics, and AI agents. Confluent, now part of IBM, is a founding member. For enterprises putting agents into production, the definition gives a name to the layer that decides whether those agents act on what is happening now or on what happened last night.
Most enterprise data architecture was built for people analyzing the business. Warehouses and lakehouses collect data in batches so analysts can report on fraud or model delivery costs after the fact. AI agents work differently. An agent that approves a transaction or reroutes a shipment acts on the state of the business at that moment, and if its data is hours old, the decision is wrong before it is made.
The research points the same way. MIT NANDA’s 2025 study The GenAI Divide found that 95% of the organizations it researched were getting no measurable return from their generative AI initiatives, and linked the gap to brittle workflows and weak contextual learning. In Confluent’s 2026 Data Streaming Report, 94% of the 4,625 IT leaders surveyed said they have seen, or expect to see, data streaming amplify the impact of their AI investments.
The Streamhouse definition sets three requirements for the architecture:
Streamhouse is a vendor-neutral category, and organizations can build one with open source technology, commercial products, or both. IBM’s answer is Confluent’s data streaming platform. It connects to the systems where enterprise data already lives, including IBM Z, SAP, Oracle, and Salesforce, then streams, processes, and governs that data as it is created. The platform keeps that context available to agents and applications through APIs, SQL, and the Model Context Protocol (MCP), and syncs it to lakehouses in open table formats such as Apache Iceberg.
The lakehouse continues to explain what happened, while the streamhouse powers what happens next. For IBM clients, that means the agents they build can work from the same live, governed view of the business that their production systems run on.
Mimacom is an IBM Silver Business Partner with a long-standing Confluent and Apache Kafka practice. Our teams have run streaming platforms in production for banks, retailers, manufacturers, and transport operators since well before the acquisition. A national railway operator retired a legacy messaging system without stopping operations. A retail chain replaced next-day stock reports with real-time inventory visibility. A bank replaced delayed account alerts with instant ones.
Each of those projects needed what the Streamhouse definition now describes: live data, built to production standards, connected to systems that were never designed for streaming. As an IBM partner, we bring that delivery experience to enterprises that want their AI agents to run on it.
If you are preparing to put AI agents into production on IBM and Confluent, our streaming architects can help you decide where your streamhouse starts and which systems it needs to connect first. Talk to our team or read our whitepaper, The Data Streaming Foundation for AI.