The AI Agile Manifesto
Agile was supposed to free us from bureaucracy. Many teams just rebuilt it with better branding. Now, AI-driven development is forcing the uncomfortable question: Were we ever truly agile, or just managing slow feedback loops?
Agile was supposed to free us from bureaucracy. Many teams just rebuilt it with better branding. Now, AI-driven development is forcing the uncomfortable question: Were we ever truly agile, or just managing slow feedback loops?
This is Part 2 of Bots and Boundaries, a three-part series on AI agents in open source.
AI is changing what small teams can ship, boilerplate gone, prototypes faster, experimentation cheaper. But lower costs of building don't mean lower costs of building the wrong thing. It just means you can do it faster.
LLMs are built for conversation, not incantations. The value isn't in your opening message, it's in the back-and-forth: clarifying, correcting, refining. Iteration is cheap. The conversation is the work.
Several AI models were given the same 36-page evidence file and the same strict instructions, no hints, no hand-holding. What followed was a revealing test of how each model actually reasons under pressure, not just pattern-matches its way to a tidy answer.
The return of multitasking, but not as we knew it. Running multiple Claude Code instances simultaneously isn't the context-switching productivity killer we've been warned about for years; it's orchestration
An AI agent recently submitted a pull request to the matplotlib Python library, and when the maintainer closed it, the agent autonomously published blog posts attacking them by name, then published an unsolicited apology. No human directed either action.
The race to AGI increasingly looks like brute-force scaling funded by a circular loop: chip makers invest in AI labs, AI labs buy their chips, valuations rise, repeat. Are we building intelligence or inflating a bubble?
LLMs generate code on demand, but they do not replace maintainers, communities, or years of shared learning. This piece explores how AI-assisted coding risks fragmenting logic, increasing technical debt, and slowly eroding the open source ecosystem.