name: cloud-deploy-gate description: "The pre-deployment gate for managed AI platforms (Azure AI Foundry, Google Vertex AI, AWS Bedrock), evals packed, budget set, guardrails on, owner named. Use before any cloud deployment." version: 1.0.0 author: Zorost Intelligence license: MIT metadata: zorost: tags: [cloud, deployment, foundry, vertex, bedrock, governance, ship] related_skills: [agent-ops-handoff, agent-loop-safety, eval-first-development] program_weeks: [18, 19, 20]

Cloud Deploy Gate

1 · Purpose

Make "deploy to the cloud platform" a checklist with an owner, so the same agent that passed locally cannot become an unbudgeted, unguarded, unowned cloud workload.

2 · When to use

• Before deploying any model endpoint, RAG app, or agent to Azure AI Foundry,

Google Vertex AI, AWS Bedrock, or Databricks Model Serving.

• When a cloud deployment already exists and nobody can answer the gate questions.

3 · Inputs

• The working local artifact with its eval scores and traces.
• The target platform account with billing access visible.
• The spec's refused tradeoffs (they now become platform settings).

4 · Procedure

1. Pack the evals. The golden set and scorer travel with the deployment. First

action in the cloud: re-run the eval there. The cloud score must match the local score within noise before anything else proceeds.

1. Set the budget alarm before the first request. Every platform has billing

alerts; set one at the week's number (e.g. $25) and one at the panic number (e.g. $100). An unbudgeted experiment is how tutorials become invoices.

1. Turn on the platform guardrails mapped to the spec. Foundry content filters,

Bedrock Guardrails, Vertex safety settings, map each refused tradeoff to its setting, in writing.

1. Pin the model version. Deploy an explicit model version/ARN, not "latest".

Record it in the spec.

1. Scope the credentials. A dedicated service account / managed identity with

least privilege: only the data sources this deployment reads. No personal credentials in any deployment.

1. Enable logging and tracing at the platform level (invocation logs, prompt/

response logging where policy allows) and verify one request shows up end-to-end.

1. Smoke-test the four failure modes: empty retrieval, over-long input,

rate-limit, and guardrail block. Each must produce the designed response, not a stack trace.

1. Name the owner in the deployment description/tag: the human who gets the

budget and guardrail alerts. No orphan deployments.

1. Write the teardown commands before leaving the page, delete endpoint,

release resources, revoke credentials. A training deployment lives exactly as long as its exercise unless its owner says otherwise.

5 · Anti-rationalization

6 · Red flags

• The eval has never run in the cloud environment.
• Billing alerts do not exist or route to a mailbox nobody reads.
• The deployed model is "latest".
• The deployment predates anyone's memory of creating it.

7 · Verify

• Cloud eval score recorded and within noise of local.
• Two budget alarms exist and route to the named owner.
• Guardrail mapping table: refused tradeoff → platform setting → verified.
• Smoke tests of the four failure modes recorded.
• Teardown commands written.

8 · ZoroLogistics example

Weeks 18 to 20 deploy the same support agent three ways. The gate is identical each time: ZoroEval re-run in the cloud (Foundry: 0.88 vs local 0.90, within noise; Vertex: 0.89; Bedrock: 0.90), $25/$100 alarms on the training subscription, refund tool behind the platform's approval/action group, invocation logging on, owner tagged, teardown scripted. The week ends with a three-platform comparison table, cost per resolved ticket, latency, guardrail fit, which is exactly the artifact a platform-selection decision needs.

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