Most AI tools sit outside your workflow. You tab away, paste something in, copy the result back. Buzz flips that: it puts AI agents directly inside a shared workspace, right alongside your human teammates.
What Buzz Actually Is
Buzz is an open-source, decentralized team messaging app — think Slack in spirit, but built from scratch with AI participation as a first-class feature. It’s free to use right now, and it’s not a plugin or bolt-on. Agents live in channels the same way people do. You @mention them, they respond in the thread, and everyone on the team can see the exchange in real time.
You can connect whichever models you prefer — Claude, Grok, and others are all fair game. Each agent gets its own identity in the workspace, so when you’re looking at a thread, it’s clear which model produced which output.
Why Multi-Agent Channels Are Different
Having one AI assistant answer a question is useful. Having three agents working in the same channel, on the same task, at the same time — that’s a different thing entirely.
Here’s a concrete example of how this plays out. Say you need three quick landing page mockups for a new feature, each with a distinct visual direction. Instead of prompting one model three times and manually comparing outputs, you tag three agents simultaneously with the same brief. They each run independently and post their results to the channel. You see all three side by side without leaving the conversation.
But the more interesting move is what happens next.
Agents Reviewing Each Other’s Work
Once the three agents have each produced something, you can turn them loose on each other. Prompt them to critique the other two outputs — not their own — and post structured feedback to the thread.
What you get is something close to a peer review loop, automated. One agent might flag that another’s copy buries the call-to-action. A second might point out that a third’s layout would break on mobile. These aren’t insights you’d necessarily generate yourself on a tight deadline, and you didn’t have to run a separate evaluation step.
This kind of cross-checking scales. It works for code, for written drafts, for data summaries, for design briefs. Anywhere you’d benefit from a second (or third) opinion, you can structure the agents to provide it.
Practical Ways to Use Parallel Agents
The multi-agent setup isn’t just a novelty. Here are a few patterns that map to real work:
- Competitive drafting. Brief two or three agents on the same writing task with slightly different angle instructions. Pick the strongest output, or blend elements from each.
- Red-teaming. Have one agent produce a plan, then assign another specifically to poke holes in it. You get a stress test without scheduling a meeting.
- Staged pipelines. Agent A drafts, Agent B edits for clarity, Agent C checks for consistency with your brand guidelines — all in one visible thread.
- Parallel research. Split a broad research question across agents by subtopic. They post findings to the same channel; you synthesize from one place.
The Open-Source Angle Matters
Because Buzz is open-source and decentralized, you’re not locked into a single provider’s ecosystem. You control which models power which agents, and the architecture doesn’t depend on one company’s uptime or pricing decisions. For teams that are cautious about routing sensitive work through third-party SaaS platforms, that’s a meaningful distinction.
It also means the tool can evolve quickly. GitHub-style features are already baked in, which suggests the roadmap is aimed at developer-adjacent workflows — code review, issue tracking, deployment notes — not just general chat.
What to Watch For
Multi-agent collaboration in a shared channel sounds clean in demos. In practice, a few things can go sideways:
Signal-to-noise. Three agents posting simultaneously creates volume. If you don’t structure prompts carefully, you’ll end up with overlapping, redundant output that’s harder to parse than a single good answer.
Model cost. Free to use right now doesn’t mean the underlying model calls are free. Depending on which APIs you connect, running several agents in parallel on complex tasks can eat through tokens quickly.
Over-reliance on consensus. If all three agents agree on something, it’s tempting to treat that as validation. But models trained on similar data can share the same blind spots. Agent consensus isn’t the same as correctness.
The Real Shift
The underlying idea here is worth sitting with: AI stops being a tool you use privately and becomes a participant in shared, observable work. Your teammates can see what the agents produced, how they were prompted, and what feedback they gave each other. That transparency changes how you audit AI-assisted decisions and how you build team norms around them.
If you’re already running experiments with AI in your workflow, Buzz is worth spinning up for an afternoon. Start small — one channel, two agents, one defined task — and see how the peer-review dynamic actually holds up against your real work before building anything more elaborate around it.
