 | Every AI agent works fine on its own — but the moment you try to use more than one, everything falls apart. Different runtimes. Different models. No shared context. No clean way to coordinate them. That fragmentation makes agents way less useful than they could be. So I started building something to run agents in one place where they can actually work together. We have plugins system and already defined some base plugins. The whole architecture is event based. Agents are defined as markdown files. Channels have their own spec.md participating agents can inject in their prompt. So basically with two main markdown files you can orchestrate workflow. Still early — trying to figure out if this is a real problem others care about or just something I ran into. How are you dealing with this right now? Open source code here: https://github.com/meetopenbot/openbot/tree/refactor/slack [link] [comments] |
We have an AI agent fragmentation problem
Reddit r/artificial / 4/8/2026
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Key Points
- The article argues that AI agents work well individually, but become significantly less effective when multiple agents are combined due to fragmentation across runtimes, models, and context handling.
- It highlights the lack of shared context and an efficient coordination mechanism as core reasons multi-agent setups “fall apart.”
- The author describes an early project intended to run multiple agents in one place, using an event-based architecture and a plugin system to enable collaboration.
- Agents are configured as Markdown files, while channels use a spec.md to define participating agents and allow agents to inject content into prompts for orchestrated workflows.
- The post invites feedback on whether this fragmentation problem is widely experienced, and links to the project’s open-source repository.
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