Wish Course

Safe deployment and rollback

How coding agents should prepare production changes without owning production authority.

Domain hubEvaluation snapshot

Learning Objectives

What this lesson should make precise

01

Name the core abstraction and its failure modes.

02

Translate the concept into a Maple/Hermon proposal contract.

03

Define at least one evaluation case that can fail the model safely.

Tutorial Flow

How this lesson becomes a demo and training target

Each tutorial is written as a user education path and a model-improvement artifact. The diagram shows how the idea moves into a lab, a typed contract, an eval gate, and then a Hermon/MapleAI demo route.

01

Concept

Deployment is a consequence

02

Applied Lab

Applied lab: Safe deployment and rollback

03

Output Contract

build_checks, restart_steps, smoke_tests, rollback

04

Eval Gate

Does the answer separate model proposal from deterministic execution?

05

Demo Route

Wish training and public model checks

01

Deployment is a consequence

Restarting a service, changing a proxy, or deploying a site affects users. Hermon Code should prepare the plan and checks; Maple authority decides whether to execute.

  • Build first.
  • Health check.
  • Rollback path.

02

Smoke tests

A deploy plan should include local service checks and external public checks. It should also scan public output for accidental internal leaks when relevant.

  • Local curl.
  • Public curl.
  • Leak scan.

03

Rollback state

Rollback must be concrete: previous commit, previous service unit, previous symlink, previous adapter, or previous static artifact.

  • Name the prior state.
  • Define trigger.
  • Verify rollback.

Lab

Applied lab: Safe deployment and rollback

Write a deployment proposal for publishing new MapleAI learning pages and Hermon media pages.

Expected result

  • A typed JSON-style proposal rather than free-form advice.
  • Clear authority boundaries and denied operations.
  • A test or rubric that decides whether the proposal is deployable.

Evaluation

How Maple would grade this work

Rubric

  • Does the answer expose assumptions instead of hiding them?
  • Does the answer separate model proposal from deterministic execution?
  • Does the answer produce artifacts that can be tested, reviewed, and rolled back?

Output contract

build_checks, restart_steps, smoke_tests, rollback, approval_required

Use this lesson as training direction

A strong lesson gives users a mental model and gives Hermon a sharper target for examples, probes, and demo prompts.

View evalsDomain hub