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July 2026

Multi-agent engineering with Claude Code: the dev loop, demonstrated

The previous post walked through a single Claude Code session — one engineer, one context window, 14 turns. That's the right shape for most work. But some tasks don't fit in one brain. A migration across 12 services, a security audit of 200 endpoints, a refactor that touches every file in the repo — these want a small team, not one engineer working 14-hour days.

Claude Code has a Task tool that spawns sub-agents with isolated context windows. The main session is the lead engineer; sub-agents are specialists. Each specialist runs to completion, returns a summary, and the lead stitches the work together. That's the multi-agent loop, in Claude Code.

This post is a full session that uses it. I'll do a real task — port a Python CLI from one config library to another across 8 files — by having Claude Code delegate parts to sub-agents. You'll see the prompts, the sub-agent invocations, where handoffs went well, and where they didn't.

The session is reconstructed from real Claude Code runs. The patterns work; the exact transcripts are illustrative.

Build Your Own Token-as-a-Service: A Self-Hosted OpenAI-Compatible AI Gateway on Kubernetes

Public LLM APIs bill you per token and leak your prompts off-prem. The fix isn't "run Ollama on a box" — it's a centralized, OpenAI-compatible inference platform your whole org consumes like a utility: one API key, token-based quotas, models that stay in your datacenter. Call it token-as-a-service. This post is the architecture — model tiering, GPU math, MIG partitioning, vLLM tuning, and KV-cache-aware routing — generic enough to run on any Kubernetes cluster with NVIDIA GPUs.

The full Claude Code dev loop, demonstrated end-to-end

Most posts about Claude Code show a single claude command and a single impressive output. That's not how you actually use it. A real session is 30+ turns: plan, write, run, fail, read the error, fix, test, commit, repeat. The interesting parts are the failures and the recovery.

This post is one full session, reconstructed from real Claude Code sessions. Every command is one you'd actually run, every code block is code you'd actually see, every bug is the kind of bug that does appear. The wall time and the exact bug-discovery sequence are approximate — the patterns are real.

The session took 14 minutes of wall time. The transcript below is annotated.

GPU DRA on Kubernetes: migrating off the NVIDIA device plugin, end-to-end

Dynamic Resource Allocation went GA in Kubernetes 1.351, and NVIDIA's DRA driver for GPUs shipped v0.4.1 on 2026-06-302. The nvidia.com/gpu extended resource model — the one the device plugin has used since 2017 — now has a sanctioned successor. The migration is non-trivial because the resource model is completely different (claim-based, not extended-resource-based), but the payoff is real: structured GPU allocation, native NVLink topology awareness, and a single API for MIG, MPS, time-slicing, and Multi-Node NVLink.

This post is the migration path: install the driver, write the first claim, swap one workload at a time, and run device plugin + DRA side-by-side until you're done.

Cilium and eBPF: the Networking Layer Under Your Cluster

Your cluster routes every packet through iptables, and at a few thousand Services that chain becomes a linear scan the kernel walks per connection. Cilium throws that out and runs networking, policy, and observability as eBPF programs in the kernel instead — no sidecars, no iptables, no agent in the data path. This post walks the whole stack: how a packet even finds another pod, installing Cilium as a kube-proxy replacement, watching real traffic with Hubble, locking the cluster down with identity-based policy, stretching it across clusters with Cluster Mesh, and dropping the service-mesh sidecars — all copy-paste.

Centralize Log Solution with the Elastic Stack

Use case: ship Kubernetes ingress logs off the cluster into a self-managed Elastic Stack on Ubuntu servers, parse them, and keep them searchable for 90 days without blowing the disk budget. The Elastic Stack handles this cleanly, and the 2026 build leans on four current features that older tutorials skip: LogsDB, ILM, data streams, and a Kubernetes-native shipper.

GPU Sharing on Kubernetes: Time-Slicing, MIG, and MPS Compared for LLM Workloads

You have 8 H100s. You have 30 inference pods that need one GPU each. The naive answer — buy 22 more H100s — is a quarter-million-dollar mistake. The right answer is GPU sharing, and there are four of them now: time-slicing, MIG, MPS, and DRA. Pick wrong and you get OOM kills, throughput collapse, or training jobs that silently corrupt each other. Pick right and you run 30 pods on 8 GPUs with measurable isolation guarantees.

This post walks each option with the actual config, the actual tradeoffs, and the decision tree that tells you which one to use for which LLM workload.

An On-Premise RAG Reference Architecture for 100 Users: Right-Sized GPUs, HA by Design

You can build a production RAG platform on your own hardware for the price of two years of API bills, and it fits in half a rack. The trick is sizing the GPUs to the models instead of buying the biggest card on the truck. This is the full design for a 100-user, highly available, hybrid-plus-graph RAG stack, with every GPU choice backed by the VRAM math.