Doubao, Kimi, WorkBuddy, or an enterprise AI management platform?

Doubao, Kimi and WorkBuddy all address the person using them, with different emphases. Trying them on everyday work tells you more than a long feature table.

Start from what employees actually do

Chat, search, writing and slides

Test with a few things that happen every day: pulling meeting material together, researching an industry question, drafting an email, building a deck. Record quality, waiting time, how many edits it took and what it cost. While the group is small, the job is to use the tool well.

Development and specialist work

Engineers, researchers and operations people need different things. Some need to read a repository and run commands; others need long documents, browser operation or specialist data. Different roles can use different tools, as long as it is clear which data may be uploaded and which accounts the company pays for.

When the question stops being which tool

The difficulty usually appears once AI enters shared projects:

• one project uses several models and agents, and files and results scatter across personal accounts;
• employees hand customer material, code or internal documents to AI for processing;
• agents can read and write tickets, CRM records, repositories or other business systems;
• the company pays centrally but cannot reconcile cost to a department, project or task;
• when people leave or change roles, accounts, prompts, working context and results cannot be handed over.

Buying more accounts lets more people use AI; it does not handle project permissions, cost attribution or handover. Microsoft keeps agent inventory, access control and audit in a separate management product for the same reason. Employee tools and company management are two selection decisions. What the management side has to cover is set out in what an enterprise AI agent management platform manages.

We manage what happens after the team starts using it

We hold employees, project workspaces, models and agents, permissions, quota and execution records in one place. Employees keep the tools that suit their role, and administrators have somewhere to handle provisioning, usage and handover.

We manage capabilities that have been connected and authorised; we cannot read the accounts and internal records of every external product. During a pilot we confirm what is supported by interface, enterprise licence and deployment conditions.

A first pass at the decision

• Only individual productivity is at stake: buy or trial one ready-made AI tool.
• Different roles need different capabilities: let several tools coexist, and set data and account rules.
• Teams start sharing projects and results: add a company workspace, member permissions and a handover path.
• Agents can operate business systems: set rules for execution identity, authorisation, human confirmation and logs.
• Cost has to be reconciled: attribute usage to people, projects, models and specific tasks.

If the first two already cover you, use the tools well. If the last three keep recurring, shared accounts, chat threads and spreadsheets get more expensive to maintain every month.

Common questions

Do a handful of people need a management platform?

No. For a few people chatting, searching or writing documents, pick whichever tool fits and use it well.

When does the question stop being which tool to choose?

When several teams share models, employees hand internal material to AI, agents can operate business systems, cost cannot be reconciled to a project, or handover breaks when someone changes role.

Can different roles use different tools?

Yes, as long as it is clear which data may be uploaded and which accounts the company pays for.

Do existing tools have to be replaced?

No. Employees keep the tools that suit their role; we manage capabilities that have been connected and authorised, and confirm the supported scope during a pilot by interface, licence and deployment conditions.