Before Building Enterprise Agents, Define Permission Boundaries

Title: Before building an Agent for your enterprise, write a task acceptance checklist first

I've been testing Feishu Bitable and Coze templates these past few days, and also compared them with Baidu Cloud's organizational adjustment announcements by actually running through the process. The news says MaaS is being folded into infrastructure while Agents are becoming independent entities. In plain English, MaaS means selling large models like utilities (water/electricity), and Agents mean letting AI handle a series of tasks on behalf of humans. I admit there's potential in this direction, but it's overheated right now; actual implementation is still early. Instead of jumping straight into automated customer service or approvals, start by writing a task acceptance checklist. Even beginners can follow this.

For the first three days, just build the table. Open Feishu, click Bitable, then New, and select Blank Table. Don't pick a template; templates have too many fields and can distract you. Enter six columns in the first row: Task Name, Trigger Condition, Input Source, Output Location, Requires Human Confirmation, Rollback Method. Trigger Condition defines when AI starts working; Rollback Method defines how to revert if something goes wrong. Fill in examples in the second row cell by cell: Task Name as 'AI organizing org adjustment announcements', Trigger Condition as 'Manual link paste', Input Source as 'Public web text', Output Location as 'One record in this table', Requires Human Confirmation as 'Yes', Rollback Method as 'Delete this row'. After entering, check the interface; Feishu auto-saves and shows sync status. The expected result is that one row explains where AI gets info, where results go, who confirms, and how to rollback on error.

By day three, move the table to real business scenarios. For example, for customer service ticket summaries: Task Name as 'CS ticket summary', Trigger Condition as 'When ticket closes', Input Source as 'Ticket body, customer notes', Output Location as 'Internal draft, do not reply directly to customer', Requires Human Confirmation as 'Yes', Rollback Method as 'Clear draft field'. This step is where beginners mess up most often. They confuse "can generate" with "can submit" as one action. The fix is to split into two columns: set it to generate only, no submission.

After a week, review logs before deciding to go live. Check three things after running for a week. 1) Human confirmation ratio. If every item needs manual edits, the Agent is just adding another step for humans. 2) Junk content ratio. Auto-updates mix in junk; cleaning is harder than scraping, so include source links and export time in fields. 3) Action scope creep. If a summary task suddenly checks order amounts, that's a red flag. My tests show that dual-agent structures often fail in unpredictable ways in real environments despite pre-planning, so don't obsess over complex architectures. Keep one task under control first.

If these three are stable, consider connecting APIs or using platforms like Coze or Qianfan for automation. If not stable, don't touch submission actions yet. No logs, no talk about launching Agents.

What to try next? Take a weekly report task or internal ticket task from your company and fill in the six columns above. Let it generate drafts only, no submissions, for three days. If after three days you can explain where each result came from, who saw it, and what changed, then talk about automation. Otherwise, it's just another hype cycle.