In this live session, I give you a first real look at the next evolution of Archon—my workflow engine for AI coding—and how I’m pairing it with my “second brain” (my Dynamis engine) to run multi-agent work in the background. The core idea is simple: I don’t want AI coding to be a one-off magic trick. I want it to be as deterministic and repeatable as we can make it, by packaging my best practices into workflows that enforce steps agents routinely forget (context fetching, validation, review, etc.). Think of it like n8n for software development: a graph of deterministic nodes (bash/CLI) and non-deterministic nodes (agents) that I can invoke on any repo. I demo what this looks like end-to-end by having my second brain create a GitHub issue, then kick off my “fix GitHub issue” workflow and monitor it until it opens a draft PR. The bigger takeaway is parallelism: with worktrees and isolated runs, I can spin up multiple workflows across the same repo (or different repos) at once, and it feels like another 10x on top of Claude Code/Windsurf-style gains. Then I tee up what I’m building next: an adversarial multi-agent harness inspired by Anthropic’s long-running agent structure—separate generator/evaluator sessions, contract negotiation, and looped scoring—because having the implementer grade its own homework is a trap.