I rebuilt my private "AI dev team" — which was secretly just a hardcoded workflow — as a substrate where orchestration emerges from instructions. Here's what I learned (and where it deadlocks).

My first version was an orchestration engine: one Python "team leader" handing out work to agents that had no idea they were on a team. It worked, but the shape of the work was baked into code and didn't generalize. So I rebuilt it as a substrate and open-sourced it: addressable agents/humans, reliable messaging, expertise discovery, memory, isolated runtimes — and left \*how the team works\* to the agents' natural-language instructions. Same primitives now run a software-eng team (delegation-shaped) and a daily magazine team (workflow-shaped); the "workflow" lives only in prompts. With async, one-way, NL messages and no shared workflow state, a team like the "magazine" one deadlocks (everyone waiting on everyone; repetition after "forgetting")… no surprise really… standard distributed computing coordination challenges. But they also self-heal in ways I hadn't scripted — started using versions for a magazine edition's drafts, messages to bring the "messaging noise" down. Questions circling in my head: Can teams of agents accomplish shared goals/tasks when instructed using natural language? Can they reach consensus the way humans do (well… mostly:)? Also, is the chatbox-based UX enough for human-agent collaboration? What are the concepts/abstractions to complement chat? Notes? Cards? Videos?