docs(chore): add draft status indicators to observability guides

Add internal-note banners indicating completion status for VAPI reviewers:

Rough Draft (3 pages - content present, needs refinement):
- observability-framework.mdx
- instrumentation.mdx
- testing-strategies.mdx

Skeleton Draft (3 pages - structure only, detailed content pending):
- production-readiness.mdx (iteration 3)
- monitoring.mdx (iteration 2)
- optimization-workflows.mdx (iteration 2)

This helps reviewers calibrate expectations for which pages are ready
for content review vs. structural/architectural review only.

Author: workingmans-ai

fern/observability/instrumentation.mdx

@@ -4,6 +4,8 @@ subtitle: Configure your assistant to capture operational and business metrics
 slug: observability/instrumentation
 ---
 
+<span className="internal-note">This page is in Rough Draft stage</span>
+
 ## What is instrumentation?
 
 **Instrumentation** means configuring your assistant to capture data during and after calls. Before you can test, monitor, or optimize, you need to define **what data to capture**.

fern/observability/monitoring.mdx

@@ -4,6 +4,8 @@ subtitle: Visualize trends, track operational health, and ensure production reli
 slug: observability/monitoring
 ---
 
+<span className="internal-note">This page is in Skeleton Draft stage - structure and scope for review, detailed content to be developed in iteration 2</span>
+
 ## What is monitoring and operating?
 
 **Monitoring & Operating** means running your voice AI system in production with continuous visibility into its health and performance. This stage answers critical operational questions:

fern/observability/observability-framework.mdx

@@ -4,6 +4,8 @@ subtitle: A systematic framework for building, testing, and improving voice AI a
 slug: observability/framework
 ---
 
+<span className="internal-note">This page is in Rough Draft stage</span>
+
 ## What is observability for voice AI?
 
 Observability for voice AI means **instrumenting your assistants to capture data**, **testing them before production**, **extracting insights from calls**, **monitoring operational health**, and **using that data to continuously improve**.

fern/observability/optimization-workflows.mdx

@@ -4,6 +4,8 @@ subtitle: Use observability data to continuously improve your assistant
 slug: observability/optimization-workflows
 ---
 
+<span className="internal-note">This page is in Skeleton Draft stage - structure and scope for review, detailed content to be developed in iteration 2</span>
+
 ## What is optimization?
 
 **Optimization** is the continuous improvement loop: using observability data to refine prompts, improve tool calls, and enhance conversation flows.

fern/observability/production-readiness.mdx

@@ -4,6 +4,8 @@ subtitle: Validate your voice AI assistant is ready for production deployment
 slug: observability/production-readiness
 ---
 
+<span className="internal-note">This page is in Skeleton Draft stage - structure and scope for review, detailed content to be developed in iteration 3</span>
+
 ## What is production readiness?
 
 **Production readiness** means your voice assistant has been systematically validated across instrumentation, testing, extraction, and monitoring **before** you deploy it to handle real customer calls.
@@ -16,7 +18,7 @@ Deploying without production readiness means:
 
 **This checklist helps you avoid those problems** by ensuring you've instrumented, tested, and validated before launch.
 
-<span className="internal-note"> Does VAPI have internal prod readiness criteria we should align with?</span>
+<span className="vapi-validation"> Does VAPI have internal prod readiness criteria we should align with?</span>
 
 ---
 

fern/observability/testing-strategies.mdx

@@ -4,6 +4,8 @@ subtitle: Validate your assistant works correctly before deploying to production
 slug: observability/testing-strategies
 ---
 
+<span className="internal-note">This page is in Rough Draft stage</span>
+
 ## Voice AI Testing Challenges
 
 **Testing** means validating your assistant works correctly **before deploying to production**. Voice AI testing prevents embarrassing failures, reduces production debugging costs, and builds confidence in your assistant.