Case study · Lab / technical validation
A heterogeneous environment combining persistent AI agents with on-demand container agents across ARM64 and AMD64/x86-64 systems, designed to make workloads repeatable, isolated, observable, updateable, and subject to human approval.
The design problem
Different AI-assisted tasks require different tools, persistence, resource profiles, access boundaries, and approval rules. A single long-lived workstation or unstructured set of scripts would be difficult to reproduce, scale, update, secure, and document.
The desired outcome
The platform is being designed so persistent agents and short-lived task agents can run across mixed hardware architectures while keeping workspaces, configuration, logging, secrets, and human-review requirements explicit.
Architecture
Ubuntu and Linux laptops, Apple Silicon systems, a multi-node Raspberry Pi 5 cluster, Docker-capable hosts, Kubernetes/K3s, and cloud-capable development environments.
Persistent Hermes and OpenClaw agents for continuing work, plus dynamically provisioned or ephemeral container agents for isolated and repeatable tasks.
Reusable container images, GitHub Container Registry, version-controlled definitions, deployment workflows, persistent workspaces, and repeatable configuration.
Logging, messaging integrations, configuration management, secrets isolation, documented policies, and human-approval controls for sensitive or externally visible actions.
Patterns are designed to run consistently across ARM64 and x86-64 while allowing workloads to be isolated, recreated, scaled, updated, and retired as needs change.
Infrastructure automation, software development, research, technical documentation, business administration, product development, and specialized task execution.
Current implementation focus
Define repeatable Linux, container, storage, networking, access, and package-management patterns across dissimilar systems.
Separate long-running agent context from disposable task execution so state is retained only where it is intentionally required.
Collect useful health and execution data while designing a later public status layer that exposes only sanitized aggregate metrics.
Keep credentials outside public artifacts and require human review for sensitive financial, customer-facing, administrative, or externally consequential actions.
Prepare architecture documentation, deployment examples, container definitions, runbooks, policies, schemas, and workflow artifacts that demonstrate the design without exposing private systems.
What this demonstrates
What this does not claim
This is active lab, technical-validation, consulting-development, and product-development work. It is not presented as ownership of a large enterprise production Kubernetes or AI platform.
Enterprise production experience is documented separately in the infrastructure automation case study and the Engineering and Leadership resumes.