Insights, updates, and technical deep-dives from the StreamFlow team.
Three architectural bets, a benchmark at 4.13M msg/s on commodity hardware, and the one thing we got wrong along the way.
The pattern production AI agent teams keep converging on, why separating the three stages matters, and how to implement it cleanly.
Model Context Protocol works beautifully in a demo. In production, tool calls fail, time out, and cascade. Here are the four patterns we use to keep them boring.
Vector databases became the default memory layer for AI agents almost by accident. For most agentic workloads, they are the wrong tool. Here is what to use instead.
Kafka is a message broker. Event meshes route decisions. The distinction matters more the moment you put AI agents on the wire.
Every streaming system needs backpressure. AI agents make it harder because the cost per message is 1000x higher and unpredictable. Here is the pattern that works.
Reimplementing the Kafka wire protocol is a solved problem. The hard part is what happens after the bytes arrive. Here is what a modern streaming runtime has to get right.
Most enterprise AI agent platforms break when they hit production scale. Here's why and what a real-time decision engine does differently.
AI agents are revolutionizing real-time data streaming, moving beyond simple collection to proactive understanding and reaction. Discover how StreamFlow enables this transformation.
