Open reference architectures
Drizzle publishes the platform patterns behind production AI so teams can examine the boundaries, trade-offs, and operating model before committing to an implementation.
Published blueprints
These are working points of view, not decorative diagrams. Each guide explains the layers, interfaces, controls, and operational concerns that need to agree for production AI to hold together.
An open, layer-by-layer reference architecture for secure AI infrastructure, model inference, agent runtimes, delivery automation, and production operations.
Study the architecture
Explore Drizzle AI Systems' open reference architecture for secure, scalable, and observable multi-agent systems in your cloud.
Study the architectureArchitecture principles
Identity, tenancy, data, network, and tool permissions shape the platform before product features do.
Delivery, policy, and observability remain independent from the models and agent workloads they govern.
Open interfaces let models, runtimes, databases, and cloud services evolve without rebuilding the whole system.
Scaling, failure recovery, cost attribution, and incident response are architecture concerns from the start.
Apply the pattern
Bring an existing design or a new workload. We will examine where the reference fits, where it does not, and what should be proven first.
Review your architecture