Agentic SDLC on Jamie's Blog

Beyond the IDE: Running Autonomous Agentic SDLC in Isolated Containers

Tue, 03 Nov 2026 00:00:00 +0000

Beyond the IDE: Running Autonomous Agentic SDLC in Isolated Containers

In Part 1 and Part 2, I shared the governance rules that keep AI coding agents on track: Dual-PR Spec-First contracts and the adaptive L0–L3 auto mode.

However, one practical problem remained: everything still runs on the developer’s laptop.

If an agent needs 10 minutes to run tests, explore AST files, and do a review pass, the developer has to keep their laptop awake, avoid switching branches, and baby-sit terminal prompts. If you try to run agents in standard CI, they either crash when asking for interactive input or make wild guesses.

Scaling Agentic SDLC: Designing an Adaptive Auto Mode for Autonomous Coding

Mon, 02 Nov 2026 00:00:00 +0000

Scaling Agentic SDLC: Designing an Adaptive Auto Mode for Autonomous Coding

In Part 1, I explained why moving from AI-first prompt engineering (VS Code + GitHub Copilot / OpenCode) to autonomous agentic coding (VS Code + Claude Code) breaks traditional software delivery, and why I built a strict Dual-PR Spec-First workflow for autonomous agents. By separating intent agreement from implementation execution, the architectural results were solid: zero contract drift, no test gaming, and strict boundary containment.

From AI-First + SDD to Agentic SDLC: Building a Dual-PR Governance Engine for Autonomous Coding

Sun, 01 Nov 2026 00:00:00 +0000

From AI-First + SDD to Agentic SDLC: Building a Dual-PR Governance Engine for Autonomous Coding

Over the past three years, software engineering underwent two quiet revolutions.

First came the AI-Assisted era. Tools like early GitHub Copilot delivered inline tab-completions. The developer remained the compiler, the architect, and the typist—AI was simply an intelligent autocomplete.

Then came the AI-First era, powered by Spec-Driven Development (SDD). Senior engineers quickly realized that casual, conversational prompting produced fragile code. To get production-grade output from large language models in environments like VS Code + GitHub Copilot and VS Code + OpenCode, the industry converged on a disciplined methodology: write formal specifications first. We learned to draft Markdown PRDs, define OpenAPI schemas, and document BDD scenarios (GIVEN / WHEN / THEN) before asking the model to write code. Feeding structured specifications into the model allowed teams to generate entire modules, database schemas, and integration suites cleanly. For teams practicing SDD, AI-first development became the gold standard of productivity.