Articles

As we increase our reliance on AI, decoupling intelligence from organization will become vitally important. The model providers are starting to build the systems through which agents communicate and coordinate; we could end up depending on them much more than we depend on the models. In May 2025, Microsoft announced task delegation between agents in Copilot Studio and an Entra directory of agent identities. In July 2026, OpenAI announced a multi-agent beta in the Responses API, with hosted actions for spawning agents, sending messages, and waiting for replies. In August, Anthropic added messaging between independent Claude Code sessions. ...

For developers who have fully embraced AI agents, productivity is no longer the bottleneck. Agents produce code faster than humans, and often better. The bottleneck is correctness: whether the code solves the problem, whether the architecture can still hold the feature set you’re growing into, whether the implementation encodes the right premises. The practical shift is that a significant fraction of your agent compute should now go to validation (tests, reviewers, scope checks), rather than to producing more code. ...

The simplest improvement you can make to your agentic programming workflow is to run two agents instead of one. One writes code in its own worktree; the other, in a parallel worktree, reviews it. They share a task list and talk after every TDD cycle. The reviewer is the holder of the lamp: it keeps the end goal fresh in context across the whole task, catching drift before it accumulates. ...

Note added on 2026-05-08: I am moving away from beadhub. Having a tool that messed up with my .git was too painful. I have been focussing all my efforts in https://aweb.ai (OSS at https://github.com/awebai/aweb) which fully replaces beads and beahdub. I wrote previously that the bottleneck in AI-assisted programming is shifting from individual productivity to coordination. I’ve spent the past several months building a tool to address that. BeadHub is an open-source coordination server that lets AI programming agents claim work, talk to each other, reserve files, and escalate to humans—across machines and across programmers. I use it daily to manage around fifteen agents working on two or three products. ...

TL/DR The conventional wisdom is that AI will reduce the demand for programmers. I manage engineering teams and program with LLMs every day, and I am seeing something different. Programmers who know how to work with LLMs can be 3-10x faster for specific tasks. Given that the size of our work backlog is only limited by our vision and ambition, these super-productive individuals will be more in demand, not less. ...