AI Agent Go-Live Checklist

AI Agent Go-Live Checklist is a practical final review before an AI agent moves from testing to real use. Use this checklist when a workflow is no longer just a private experiment and may affect users, content, cloud costs, support messages, files, accounts or production systems.

The safest beginner rule is simple: an AI agent should not go live until it has logs, limits, human approval, a rollback path and a clear definition of what it is allowed to do.

Interactive Go-Live Checklist

Is your AI agent ready to go live?

Check the controls you already have. The score updates automatically and tells you whether the workflow is ready for a private test, controlled pilot or public launch.

One narrow task is defined Human approval is required before sending, publishing or changing anything API spending limit or budget alert is active Logs capture input type, tool use, output and approval result Private data is minimized, hidden or excluded Rollback steps are written API keys can be revoked or rotated quickly Failure cases were tested The agent cannot run unlimited loops Someone owns monitoring and review

Readiness score 0/10

Not ready: keep the workflow private and draft-only until basic controls exist.

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All Model APIs GPU Cloud VPS / Hosting Agents Pricing

OpenAI API PricingModel pricing, tools, realtime and token costs. Claude API PricingClaude model and prompt cache pricing. Gemini API PricingGemini API pricing and billing details. DeepSeek PricingDeepSeek model pricing and discount windows. DigitalOcean DropletsVPS pricing for apps, APIs and workflows. DigitalOcean GPU DropletsGPU cloud pricing and configurations. RunPod PricingGPU pods and serverless GPU pricing. Vast.ai PricingMarketplace GPU pricing. Hostinger OpenClawBeginner-friendly OpenClaw deployment. OpenClaw VPS TemplateOpenClaw on VPS and Docker context. AWS AgentCore PricingAgent runtime, tools, memory and gateway pricing. AWS Bedrock PricingManaged foundation model pricing on AWS.

Go-live decision: should this agent be public?

Question	Safe answer before go-live	If the answer is no
Does the agent have one clear task?	Yes, the workflow has a narrow goal and known output type.	Keep it in testing.
Can the agent spend money?	No, or spending is limited and monitored.	Add billing limits first.
Can the agent publish, send or delete?	No for beginners, or only with human approval.	Switch to draft-only mode.
Are logs enabled?	Yes, every run records input type, tool use, output, approval and failure reason.	Add logging before launch.
Is there a rollback path?	Yes, you can disable keys, stop the server, restore backup or disconnect webhooks quickly.	Create rollback instructions first.
Is private data protected?	Yes, sensitive input is limited, hidden or excluded.	Fix privacy handling before launch.

24-hour pre-launch checklist

1. Define the agent level. Decide whether the workflow is Level 0 draft-only, Level 1 suggestion-only, Level 2 approval-required or higher.
2. Freeze the first use case. Do not add extra tools or extra channels during the final test.
3. Set API spending limits. Add provider-side limits, alerts or budget monitoring before other users can trigger runs.
4. Check rate limits. Confirm what happens if the API returns errors, timeout, quota exceeded or rate-limit responses.
5. Test failure cases. Try empty input, confusing input, private input, long input and unsupported requests.
6. Review permissions. Start with read-only or draft-only access wherever possible.
7. Confirm human approval. Any send, publish, delete, payment, account change or production change should require review.
8. Verify logs. Logs should show what happened without exposing secrets, passwords, API keys or private customer data.
9. Create rollback instructions. Write the exact steps to disable the workflow, revoke keys, stop the server or restore a backup.
10. Run one final realistic test. Use a real-world example, inspect the output and confirm the workflow behaves as expected.

Minimum logging fields

Log field	Why it matters
Timestamp	Shows when the run happened.
Workflow name	Separates support, content, monitoring and other workflows.
Input category	Shows what type of request triggered the agent without storing unnecessary private details.
Model used	Helps debug cost, quality and latency problems.
Tools used	Shows whether the agent searched, called an API, read files or prepared an action.
Output type	Draft, suggestion, alert, summary, classification or action request.
Approval status	Approved, rejected, edited, escalated or blocked.
Error reason	Needed for debugging and incident review.
Estimated cost	Helps detect expensive loops or unexpected usage.

Rollback plan template

workflow_name: support_reply_draft
owner: site_admin
risk_level: Level 2 approval-required
rollback_steps:
  - disable public trigger
  - revoke or rotate model API key
  - stop VPS service or container
  - disconnect webhook or channel integration
  - restore last known good backup if needed
  - review logs before re-enabling
emergency_contact: site_admin

Red flags: do not go live yet

• The agent can send messages without review.
• The agent can publish content publicly without review.
• The agent can delete, overwrite or edit production data.
• The agent can use paid APIs without spending limits.
• The agent logs private data, API keys or passwords.
• The workflow fails silently.
• No one knows how to disable it quickly.
• The project owner cannot explain the monthly cost risk.

GPUJet rule: a beginner agent is ready to go live only when it is useful, limited, logged, reversible and approved by a human for risky actions.

Continue learning

• AI Agent Risk Levels
• AI Agent Safety Checklist
• AI Agent Logs
• AI API Cost Control Tutorial
• AI Infrastructure Hub