AI Config Update & Lifecycle

Prerequisites

• Existing AI Config to modify
• LaunchDarkly API access token or MCP server

Core Principles

1. Fetch Before Changing: Always check the current state before modifying
2. Verify After Changing: Fetch the config again to confirm updates were applied
3. Archive Before Deleting: Archival is reversible; deletion is not

API Key Detection

1. Check environment variables —

   LAUNCHDARKLY_API_KEY

   ,

   LAUNCHDARKLY_API_TOKEN

   ,

   LD_API_KEY

2. Check MCP config — If applicable
3. Prompt user — Only if detection fails

Workflow

Step 1: Understand Current State

GET /projects/{projectKey}/ai-configs/{configKey}

Step 2: Make the Update

• Update instructions/messages — PATCH variation
• Switch model — PATCH variation with modelConfigKey and model
• Tune parameters — PATCH variation with model.parameters
• Archive config — PATCH config with

  {"archived": true}

• Delete — DELETE config or variation (irreversible)

Step 4: Verify

1. Fetch updated config:
   bash

   GET /projects/{projectKey}/ai-configs/{configKey}/variations/{variationKey}

2. Confirm the response shows your updated values
3. Report results:
   • ✓ Update applied successfully
   • ✓ Config reflects changes
   • ⚠️ Flag any issues or rollback if needed

What NOT to Do

• Don't update production directly without testing
• Don't change multiple things at once
• Don't skip verification
• Don't delete without user confirmation

Related Skills

• aiconfig-variations

  — Create variations to test changes

• aiconfig-tools

  — Update tools

References

• API Quick Start