GenAI-Based Development Platform - Part 3: Announcing Isolarium, Three Flavors of Secure Sandboxes for GenAI-Based Coding Agents
Open‑source project Isolarium was announced as a companion to the Idea‑to‑Code workflow, providing secure sandboxes for GenAI coding agents such as Claude Code. The tool lets developers run agents in three isolation modes—Nono (lightweight), container, and virtual machine—balancing security against performance overhead. By executing agents in disposable environments, Isolarium mitigates credential exfiltration, malicious container execution, and unintended host modifications. Integration with the i2code implement command extends the protection to the final code‑generation step.
Microservices Platforms - Part 6: Build Platform
The sixth article in the Microservices Platforms series introduces the Build platform, a core component that, together with the Deployment platform, maps the journey of code changes from a developer’s laptop to production. It outlines how the Build platform automates...
GenAI-Based Development Platform - Part 2: How Idea to Code Turns an Idea Into Working, Tested Software
The article details the "i2code implement" subcommand, which orchestrates Claude Code to turn a structured plan into a production‑ready pull request using test‑driven development. It combines deterministic Python setup with AI‑driven code generation, handling setup, recovery, and a repeatable implementation...
GenAI-Based Development Platform - Part 1: Guardrails
The article introduces a GenAI‑based development platform, dubbed Harness, that layers deterministic guardrails around coding agents such as Claude Code. It outlines four protective mechanisms—pre‑commit checklist skill, pre‑commit Git hook, GitHub Actions workflows, and automated pull‑request reviews—to catch errors and...
Why GenAI-Based Coding Agents Are a Complex Domain in Cynefin - and What that Means for Adoption
The piece frames generative‑AI coding agents as a complex problem space within the Cynefin framework, emphasizing that prompt‑to‑output behavior is inherently unpredictable. Unlike traditional developer tools that sit in clear or complicated domains, LLM‑driven agents require safe‑to‑fail experiments, rapid feedback,...
Microservices Platforms - Part 5: Observability Platform
The fifth installment of the Microservices Platforms series introduces an Observability platform that centralizes metrics, logs, and tracing for microservices. It explains how a dedicated platform team delivers shared observability capabilities, allowing service teams to concentrate on their core domain...
