The author describes the hassle of manually coordinating multiple AI coding agents and introduces Accord Agents, an open-source shared workspace that enables agents to discuss and review each other's work while keeping the process transparent to the human.
For about a week, my “multi-agent workflow” was embarrassingly low-tech. I’d build a plan with one model - say, Claude Code - and then, because I wanted a second opinion, paste the whole thing into Codex to see whether it would catch anything the first one had missed. Same dance for PR reviews: one model reviews the diff, then I hand the same diff to another for a second read. Moving the context between them was not really the problem. Copying a plan or pointing both agents to the same files is easy. The problem started when the second model came back with ten suggestions. I still had to go through every one of them, decide whether it was valid, figure out whether it was based on missing context, and work out whether the first model would agree or push back. At some point, I realized I was not actually saving much time. I had simply turned myself into a messenger and referee between AI agents. What I really wanted was for them to discuss the work directly: challenge each other’s assumptions, validate the proposed changes, and agree on what should actually be added to the plan or fixed in the code. But I did not want that discussion to happen invisibly inside a black box. I wanted to see the conversation, understand why a suggestion was accepted or rejected, and step in whenever I disagreed. There was another problem too. When an agent is working on something for 20 or 30 minutes, I am obviously not going to sit there and stare at the terminal. I start another task, which means opening another agent. Then perhaps I want a second opinion on that task as well. Before long, I have a collection of terminal windows and sessions, all working on different things and all at different stages. Which agent is doing what? Has this plan already been reviewed? Were those suggestions ever validated? Which session am I supposed to check next? The more agents I used, the more time I spent coordinating them. That is what led me to build Accord Agents: a shared workspace where multiple coding agents across different providers can work on the same task, talk to one another, review each other’s work, and keep the entire process visible to the human. Purely vibe coded open source project. The goal is not to remove the human from the process. It is to stop making the human manually carry every message between the agents. I’m curious whether anyone else using multiple coding agents has run into the same problem. How are you organizing this workflow today?
The author experimented with getting two AI agents, Gemini and Copilot, to talk via voice mode, finding it fascinating but needing refinement, and speculated that emergent AGI could arise from linked agents.
A field report on an open-source experiment testing how multiple AI coding agents share a codebase: isolated branches produced semantically conflicting but textually mergeable work in every run, while a shared working directory and a lightweight 'decision model' referee (Jev) improved coordination, and unsupervised agent-to-agent messaging emerged on its own.
The author built AgentsHive, a shared workspace that coordinates multiple AI agents (like Claude and Codex) into a collaborative product team with roles, memory, and routing, allowing solo builders to avoid manually juggling separate agent workflows.
This article explores the limitations of personal AI agents in interoperability, noting their inability to coordinate with other agents or systems, and questions whether current technologies can reliably address this gap.