Design proposal: This page preserves exploratory architecture ideas, not verified runtime capabilities. Persistent Neptune integration, Karma CLI/service commands, coordinated changes, and learning systems are not implemented end-to-end in the current source. Read the implementation status and proof criteria first. Consequential AWS execution requires separate human authorization.

Coordinating Change in Karma #

Karma can accept configuration change requests — but in a system with dependencies, propagation must be coordinated carefully to maintain system integrity.

This page outlines a proposal for managing graph-aware change requests safely.

The Problem #

Changing the config for one component might:

Break downstream consumers if done out of order
Require reloading or redeploying related components
Involve validation before it’s safe to apply

Karma’s Role #

Karma should:

Receive a change request
Analyze impact in the graph
Determine what downstream actions are required
Apply the change and update the graph (or queue it)
Log the request and outcome for auditing

Change Lifecycle (Proposed) #

Client → karma-api: POST /request-change
               ↓
         Karma validates config
               ↓
         Karma builds proposed graph patch
               ↓
         Karma checks for conflicts or blocking dependencies
               ↓
         If safe: write to config + update graph
         Else: reject or queue for later

Use Cases #

Scenario	Karma Behavior
Change VPC subnet config	Reject if dependent EC2 instance is running
Update DNS aliases	Allow if CloudFront outputs are available
Remove shared bucket reference	Block if downstream Lambda still depends on it

Request Format (Example) #

{
  "component": "serverless-site/karma-dev",
  "proposed_change": {
    "cloudfront_aliases": ["www.usekarma.dev"]
  },
  "requested_by": "ted@strall.com"
}

Result Format #

{
  "status": "applied",
  "affected_components": [
    "route53-zone/karma-dev",
    "cloudfront-distribution/karma-dev"
  ],
  "warnings": [],
  "log_entry_id": "log-1fd742f"
}

Implementation Notes #

Start with read-only observation and reviewable proposals; add a separately authorized execution path only after validation
Validation and rollback paths are key to correctness
A dry-run or “plan” endpoint should be available

In the future, Karma can act as a safe graph mutation gateway — analyzing dependencies, proposing valid transitions, and enabling dynamic reconfiguration through a structured request model.