How to Run a Swarm of Agents Within Alibaba

As someone completely clueless, I tried Alibaba Cloud's "Wanyou Wujie" (Boundless Collaboration), which entered public beta on September 2nd. It's defined as an enterprise-level "Human-Agent Collaboration Platform." The core isn't single-chatting, but breaking down complex tasks among multiple Agents, while humans monitor progress, adjust directions, and retrieve results within the loop. News headlines call it another "Agent horse race" for Alibaba, but my experience suggests it's more like adding a collaboration shell over enterprise processes. I'll note this down from a beginner's perspective; I can't guarantee it matches every enterprise account exactly, but the path should generally work.

Don't Rush to Create Projects

1. Log in to the Alibaba Cloud console, type Wanyou Wujie in the top search bar, and find the public beta entry. Seeing the application page or product homepage means your account has access; no entry usually means the entity, permissions, or beta eligibility aren't enabled.

2. Once inside, create a Project. Name it something like "Competitor Weekly Report," and clearly state the goal: produce a one-page summary every Friday including sources, viewpoints, and risks. Expect a project panel here with tasks, members, and an Agent list.

3. Break tasks into verifiable small blocks. For example, "Data Collection," "Summary Generation," "Risk Verification," and "Formatting & Export." Don't write "Help me write a weekly report"—it's too vague, and Agents tend to improvise. Expect to see multiple task cards, each assignable to different Agents.

4. Assign an Agent to each task and input role descriptions. For the Data Collection Agent, write: Extract facts only from specified folders or links; do not evaluate. For the Summary Agent, write: Output in three sections. For the Risk Agent, write: Flag uncertainties. Expect Agent status to change from "Pending" to "Running."

5. Humans shouldn't just be spectators. After tasks start running, add a comment like "Don't write conclusions here; list sources first." Expect human feedback to remain in the project, which subsequent outputs will reference or address.

6. Finally, click export or archive to put results back into the enterprise account. Only when you see "Accumulated" or downloadable records is it considered complete.

Pitfalls mainly occur in three areas. If the account isn't real-name verified or lacks enterprise permissions, you can't even click the entry; you must ask an admin to enable it first. Task descriptions that are too natural-language-like cause Agents to drift; you must break them down into inputs, outputs, and acceptance criteria. If permission boundaries aren't set well—one Agent seeing all company data vs. another seeing only a folder—results often fail or become messy. Here, patience with permission boundaries is more important than patience with model capabilities.

The advantages are straightforward. It turns "a group of Agents working" into a project workflow: tasks can be split, humans can intervene, and results are accumulated. For teams already using Feishu, DingTalk, or Jira for project management, this format is smoother than separate chat windows. The downsides are obvious too: It doesn't automatically generate good workflows. If your original work is chaotic, multi-Agents will just fragment the chaos further. The term "AI Employees" is still exaggerated; what's truly useful is chaining together data, summaries, verification, and archiving.

My judgment is that Wanyou Wujie is suitable for testing small workflows, not for taking over core business immediately. Start with low-risk tasks like weekly reports, meeting materials, or competitor summaries for two weeks to see if it can consistently leave records, then discuss scaling up. Next, try "Meeting Materials": break recording transcription, agenda organization, action item extraction, and minute archiving into four Agents. The real question is: How much context are enterprises willing to let a group of Agents see?