The infrastructure we're building toward

Foundations for agents that can plan, use tools and operate with predictable guarantees, permissions and auditability rather than open-ended autonomy.

Investing in the Model Context Protocol surface so agents and applications can discover, connect to and safely exchange context with tools and data sources.

Exploring the primitives — scheduling, memory, identity and messaging — that let many models and agents operate together as one coherent system.

How independent agents and services address, route to and trust one another across organisational and network boundaries.

Durable coordination of complex pipelines across models and tools, with retries, guardrails and human oversight where it matters.

Tracing, evaluation and monitoring built for probabilistic systems, so teams can debug behaviour and measure reliability over time.

Well-documented, versioned interfaces that expose infrastructure primitives without leaking underlying complexity.

Reliable automation of real workflows, with the safety, observability and controls that production environments require.

The compute, state and networking foundations for running agentic and model-driven systems at scale.

Early research into how the same coordination and communication layer can extend to robotics and embodied systems.

A developer experience — SDKs, docs and references — that will accompany our infrastructure as capabilities mature.

The systems that let AI agents, models, applications and machines communicate, coordinate, operate and scale.