Building Proactive, Full-Context AI Agents Doesn’t Have to Be Complicated
Discover how event streaming and the lakehouse come together to power smarter agentic workflows.
Event stream
Apache Kafka
payments.events
Checkout failed 3 times in 5 minutes
Lakehouse
Apache Iceberg
crm.customer_history
Premium customer for 4 years, one prior escalation
Streambased
Unified view
One query across live events and full history.
- Zero copy
- No pipelines
- Just SQL
AI agent
Customer support
- Triggered by a live event
- Joined with full history
Action taken
Opened a priority ticket and sent a retry link before the customer got in touch
Illustrative examples
Agents need event streaming!
Event streaming makes applications responsive and delightful.
“Your taxi will be there shortly” is good,
The same is true for AI agents.
Reactive, narrowly scoped and out-of-date agents are OK,
How Event Streaming Powers Agentic Workflows
It starts with a stream.
Agent workflows should be triggered by event streams. A decade of stream processing means the stream is the perfect place to detect threshold breaches, anomalies or plain and simple inputs as soon as they are available.
But triggering an agent is only half the story.
Agent queries are unpredictable, so agents need wide-ranging and flexible access to data to satisfy them. This comes from the lakehouse, but there’s an important catch.
The event that triggered the agent isn’t in the lakehouse yet.
Agents need a unified view of both the latest events on the stream and historical data in the lakehouse to work with full, up-to-date context.
Event stream: T0: event arrives
Event stream to Stream processing: Process the live event
Stream processing: Detect condition of interest
Stream processing to AI agent: Trigger investigation
Historical and live events in one view
AI agent to Unified view: Investigative query including trigger event
Unified view to AI agent: Result includes the triggering event immediately
Historical context plus live evidence in one query
Two Changes to Make This Work
Building agentic architectures that combine event streaming and the lakehouse requires two changes.
Shift Left: Create Silver Streams
Traditionally, event streams land in the lakehouse as bronze data before being transformed into clean, reliable silver data through ETL.
For agentic workflows, this needs to happen earlier. We need silver streams that are clean and reliable from the earliest possible point.
Many organisations are already doing this to some extent, but agentic workflows make it more important than ever.
Unify the Stream and the Lakehouse
Agents need a unified view of silver topics and silver tables to work with full context.
That means access to everything from events that happened milliseconds ago on the stream to years of historical data in the lakehouse.
One view, combining real-time and historical data, for agents to perform their tasks.
The central architectural components are:
Kafka as the ingestion point: Events enter the platform through Kafka.
Streambased gives your agents one view of the stream and the lakehouse
Silver topics in Kafka hold what just happened. Silver tables in Iceberg hold everything that came before.
Streambased makes Kafka and Iceberg queryable together as a single view, so agents can investigate a triggering event alongside its full history, without copying data, building new pipelines or changing the tools your teams rely on.
There’s no waiting for the lakehouse to catch up. The event that triggered the agent is already in the view, together with the context it needs to act.
Unified view
Watch it sweep across time - or drag it yourself
Streaming straight from Kafka - no delay, no pipeline.
Live
+ historical
1
unified table
0
data duplicated
The Future of Agentic AI Is Context
Building better agentic architectures doesn’t require throwing away the technologies and practices we already know and trust.
It’s about bringing them together in a new way.
Want agents that are:
Proactive — acting on events as they happen.
Flexible — accessing the data needed for unpredictable queries.
Current — working with the latest information and historical context.
You need:
Silver Streams
Clean, reliable real-time data that agents can act on immediately.
A Unified Streaming and Lakehouse View
Seamless access to live events and historical data through a single view.
Plug directly into your existing stack
Integrate directly with Kafka and analytics tools and move from data to action instantly.
Insights Hub
Stay ahead with perspectives on real-time analytics, streaming architectures, and industry trends.
Three problems, one solution
Streambased unifies Kafka and Iceberg into a single, queryable view, live and historical data together, without moving or copying anything. That one capability can answer a wide range of different data challenges.
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