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In the second part of the series, I dive into the technical coordination of my AI team, specifically using the local filesystem as a shared state mechanism. I explain how my sub-agents (responsible for tasks like research, coding, or review) communicate by reading and writing files in a structured way.
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This post details my transition from basic LLM prompting to a more structured “AI Team” approach. Frustrated by the limitations of single-prompt interactions, I built a system of specialized local agents using tools like Ollama and Llama 3. My focus is on creating autonomous agents that can handle specific parts of a workflow.
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I explain the benefits of a “Kubernetes-on-Kubernetes” architecture using Rancher and Harvester. I use Harvester as a bare-metal hyper-converged infrastructure (HCI) solution built on KubeVirt and Longhorn, providing a stable base for my virtualized workloads. Rancher then sits on top to orchestrate and manage multiple downstream Kubernetes clusters.
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In this article, I explore how to implement seamless cluster autoscaling in RKE2 environments using Rancher’s Node Drivers and MachineDeployment resources. I highlight the use of the upstream Kubernetes Cluster Autoscaler (CA) as the core engine, which communicates with Rancher to dynamically provision or terminate nodes based on pod demand.