AI agents are moving from answering questions to taking action, and to do that effectively, they need to know the state of the business at any given moment. Most data architectures still aren’t designed to support querying subsecond updates across the enterprise, which is why the streamhouse has emerged to address this gap.
The streamhouse is a data architecture pattern that continuously delivers the current state of a business to applications and AI agents. A group of data infrastructure companies defined it to provide consistency around an approach many organizations have started to deploy independently anyway.
A streamhouse can be built from open-source technologies such as Apache Kafka, Apache Flink, and Apache Iceberg, from commercial products, or from a combination of both. The group behind it, which includes Confluent, Aiven, StreamNative and Ververica, has published an open, vendor-neutral definition at streamhouse.com, and anyone may use the name free of charge.
A common definition gives architects and data teams shorthand for what capabilities they need and where they sit relative to the rest of the stack. When a data engineer says “we need a streamhouse,” the CFO and other stakeholders can immediately understand what they mean and grasp the value.
A different kind of architecture
Most data architectures were designed to support business intelligence, where data is processed in batches for human analysis. The lakehouse does that job well and remains the right foundation for it. Agents need something different: data that reflects what’s happening now, delivered at machine speed.
“Agents need something different: data that reflects what’s happening now, delivered at machine speed.”
That calls for an architecture that continuously captures, processes, governs, and serves a real-time view of the business, so that applications and agents can act as events occur.
Use cases include order fulfillment, claims processing, underwriting, and fraud detection, all of which rely on real-time data from multiple sources. A claim might draw on policy records, payment history and customer interactions, and joining those sources as events happen is where the architecture is most valuable. Hyper-personalization in e-commerce and gaming are other examples.
What defines a streamhouse?
The open definition sets out three attributes. First, a streamhouse is real-time. Data stays current as events occur, rather than being refreshed on a batch schedule.
Second, it’s production-native. Analytical systems often lack the fault tolerance and service levels required for operational systems, because they were never intended to power the business. A streamhouse sits in the path of business-critical applications and agents, so it has to meet the same standard as those systems.
The third attribute is decentralization. A centralized model ingests data into one place and transforms it there, making it difficult and costly to reuse for anything besides analytics. A streamhouse transforms data to make it discoverable and contextualized, so that data produced once can be consumed many times by agents, applications, and dashboards alike.
Shaping data for AI
Freshness is one part of the problem, but data shape matters too. For decades, enterprises stored every fact exactly once in highly normalized relational tables. Storage was expensive, and normalization protected developers from update and deletion anomalies. As a result, many domain relationships are encoded in tables made up of little more than two foreign keys. That structure means very little to an AI model.
“…many domain relationships are encoded in tables made up of little more than two foreign keys. That structure means very little to an AI model.”
Consider an agent that drafts a follow-up email after a customer places an order. It needs to be certain that the customer lives at a particular address and just ordered a particular product, and it needs to know the moment the order is placed. Streaming captures those changes as events, enriches them with context, and delivers them to the agent in real time.
Delivering context in that structured, queryable form also limits how much extraneous information each model call needs, reducing costs.
Where the streamhouse fits
None of this makes the lakehouse obsolete. The two architectures will coexist in many organizations, and open table formats allow them to operate from a single, consistent view of data. The lakehouse is still the right place for historical analysis, reporting, and model training, where hours-old data is acceptable. The streamhouse handles the real-time, operational workloads the lakehouse was not designed to serve.
“With today’s ML models capable enough for most business tasks, the data agents act on will increasingly determine how well they perform.”
With today’s ML models capable enough for most business tasks, the data agents act on will increasingly determine how well they perform. For teams moving agents into production, a practical first step is to list the decisions those agents will make and identify where the data behind each one lives today. In most enterprises, that data is spread across several operational systems and reaches the analytics stack hours later. A streamhouse is designed to close that gap.
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