Practical, production-minded thinking on how to build and run AI agent systems safely. We publish the architecture, not just the conclusions, so engineers and platform teams can evaluate the ideas in full.
A reference architecture for running AI agents as first-class, policy-controlled Linux workloads. It composes systemd, Unix users, cgroups, namespaces, Landlock, seccomp, structured memory, MCP, A2A, human approvals, and audit logs into a practical foundation for safe agent swarms, with no new kernel required.
A practical guide to building and running AI agents in production: frameworks, MCP, memory, orchestration, local deployment, cost optimization, safety, evaluation, and a 90-day production playbook.
More whitepapers are on the way. If you are working on agent infrastructure and want to compare notes, book a consultation.
