4 posts tagged with "methodology"

In From Cloud Native Apps to AI Native Agent Platforms: The Belts Are the Problem, I used the factory electrification story to make an argument about AI platform adoption: factory owners in the 1890s replaced steam engines with electric motors and kept the same belts, shafts, and building layouts. For thirty years, productivity barely moved. The breakthrough came when they reorganized the factory around the new technology — workflow-first, not power-transmission-first.

That post argued at the platform layer: the decisions organizations make about how to architect and run AI-native applications. The electrification analogy there was about keeping the wrong infrastructure assumptions while adopting new technology.

This post is one layer down — the development lifecycle itself. What happens to a team's coordination model when the implementation loop accelerates by an order of magnitude? The same pattern applies: if the team keeps the existing process assumptions while individual engineers adopt AI-accelerated workflows, the system neutralizes the gain.

With OpenSpec + Superpowers + Harness, I've run enough iterations to say the individual story is real. Features that used to take 2-3 days take hours. The workflow knows what done means. I'm not watching in between.

Then someone on the team wanted to use the same workflow. That's when I found out where the bottleneck had moved.

Anthropic's engineering team published a post on harness design for long-running apps. One line stopped me:

"Generators self-assess poorly — confident praise even for mediocre output."

I've been running OpenSpec's apply phase with a code review skill baked in. It runs in the same agent context as the implementation. The reviewer and the implementer are the same session. I read that line and immediately knew: that's my problem. The reviewer runs inside the same context that just wrote the code. The confidence is real. The bias is invisible.

That was the crack. This post is the fix.

I published Stacking OpenSpec and Superpowers on April 19th — three weeks ago. The headline number was concrete: a refactor shipped in three hours, eighty-six new tests, zero regressions. I meant every word of it. At that time, that stack — OpenSpec's propose/apply/archive triplet (the framework also offers explore, but I'd been skipping it) over the three Superpowers skills I lean on most (brainstorming, test-driven-development, requesting-code-review) — was the best discipline I'd found for spec-driven development with AI in the loop.

What I didn't say — because I didn't know yet — is that the stack had five hidden cracks. They didn't show on a single refactor. They showed up the third, fourth, fifth time I ran it on a new project, when the workflow had to carry weight rather than win a sprint.

This post is the evolution. It's what I'd add to that earlier post if I had to write it again today, after running the stack across a couple more projects — most cleanly on python-agent, where I deliberately let each friction point speak for itself before designing the fix.

I'll spend the first half listing the five friction points (where the earlier stack hurt) and the second half on what I did about them (a three-step methodology abstraction plus four command-level fixes). Then I'll show the four-command, four-phase shape I ended up with, why this is still agile in the form you'd recognize, and the plugin I packaged so you can run the whole thing without re-reading either post.

If you haven't read the earlier post, the original entry point is there. This post assumes OpenSpec's four-command base (explore/propose/apply/archive) and the three Superpowers skills I just mentioned (brainstorming, test-driven-development, requesting-code-review).

One thing I want to flag up front: nothing in the evolution is a clever invention. Each fix was a response to a specific moment where the earlier stack produced output I didn't trust. The pattern that emerged is practice → friction → fix → harden. Methodology evolves by being run, not by being designed. The plugin at the end is the byproduct, not the goal.

This is part 4 of a 6-part series on Claude Code.

4. Evolution of Software Development Methodologies in the AI Era​

4.1 Agile Development: From Sprints to Prompts​

Challenges of Traditional Agile​

Typical Scrum Process: Sprint Planning → Daily Standup → Development (2 weeks) → Sprint Review → Retrospective