The agent publish checklist
AI agents are becoming easier to build and share. Before one reaches a team, use this checklist to make sure it is clear, bounded, testable, and safe to run.
The newest AI product updates point toward a practical shift: agents are moving from personal helpers into shared workflows. OpenAI's latest Enterprise and Edu release notes add more controls for workspace agents, including model choices, publishing permissions, guided setup, and Slack thread behavior. Anthropic's Stainless acquisition reinforces the same direction: agents are only useful when they can connect reliably to tools and APIs. OpenAI's enterprise scaling guide also emphasizes that workflow design and oversight matter as much as raw model capability.
That means the question is changing from "Can I build an agent?" to "Is this agent ready for other people to trust?"
The skill
Use an agent publish checklist before you share an AI agent with teammates, schedule it to run, or connect it to business systems. The goal is not paperwork. The goal is to catch vague instructions, risky permissions, missing review steps, and unclear ownership before the agent becomes part of real work.
Agent publish checklist
Agent name:
{clear name}
Job:
{one sentence describing the work}
Inputs:
{what the agent is allowed to read}
Tools:
{apps, files, systems, or APIs it can use}
Outputs:
{exact format people should expect}
Stop conditions:
{when the agent must ask for approval}
Review owner:
{person or role responsible for checking results}
Test set:
{three realistic examples before publishing}The five gates
Before publishing, check the agent against five gates:
- Purpose: Can someone describe the agent's job in one sentence?
- Boundary: Are the agent's inputs, tools, and forbidden actions explicit?
- Output: Is the result shaped for real use, not just a friendly chat answer?
- Review: Is there a human checkpoint before money, customers, files, or systems change?
- Evidence: Has the agent passed realistic tests, including one messy example?
A worked example
Imagine a team wants to publish a "weekly customer feedback agent." It reads tagged support tickets and creates a summary for product planning.
Agent name:
Weekly customer feedback brief
Job:
Summarize the top product themes from tagged support tickets each Friday.
Inputs:
Support tickets tagged product-feedback from the last seven days.
Tools:
Support ticket search and the product planning document.
Outputs:
- Top 5 themes
- Evidence links
- Customer quotes
- Severity
- Suggested next review owner
Stop conditions:
Do not update the product roadmap.
Do not message customers.
Ask before using tickets outside the approved tag.
Review owner:
Product operations lead.
Test set:
1. Normal week with 40 tickets
2. Messy week with duplicate tickets
3. Sensitive ticket containing private customer dataThe prompt
Use this before sharing an agent:
Review this AI agent before I publish it to a team.
Agent instructions:
{paste instructions}
Connected tools and data:
{paste tools, files, apps, APIs, or permissions}
Expected output:
{paste output format}
Evaluate it against:
1. Purpose clarity
2. Input and tool boundaries
3. Stop conditions
4. Human review points
5. Output usefulness
6. Failure modes
7. Three tests I should run before publishing
Return:
- Publish readiness: ready / needs changes / do not publish
- The top 5 changes to make
- A revised agent instruction blockCommon failure modes
- Too broad: "Help with sales" is not an agent. "Draft a weekly lead follow-up queue from approved CRM fields" is closer.
- Hidden permission creep: The agent can read more than people realize, or can act in systems where it should only draft.
- No messy test: It works on clean examples but fails on duplicates, missing context, sensitive data, or conflicting instructions.
- No owner: Everyone likes the agent, but nobody is responsible when its output is wrong.
- Output drift: The answer format changes week to week, so people stop trusting it as a workflow component.