Conductor by Melty Labs makes parallel agent workflows practical by running multiple agents with separate tasks simultaneously. The trade-offs are real but manageable, and this is where development is heading.
A couple of months ago, I was copy-pasting prompts into ChatGPT. Now I'm shipping features, running tests, managing branches, and keeping documentation alive, with a team of agents doing the heavy lifting. All by myself.
Legacy codebases are messy, undocumented, and full of decisions nobody remembers making. But if you can explain it to a new developer, you can onboard an AI and that changes everything.
Software development's feedback loop has compressed from years to minutes, but QA remains the last bottleneck, the one place still dependent on human judgment. AI is rapidly closing that gap, and before the year is out, that final human checkpoint may no longer be necessary.
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.
Part 2 of the article about Mossie, when it was faced with scaling to include every bird in the world, complete with photos, sounds, and icons.
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.
AI made writing code faster, but the real economics of software engineering were never about typing code in the first place.
