Add AI Advisory Services for Higher Education blog assets

[langfordzl]

Summary

Adds companion code for the Databricks blog post "AI-Enabled Advisory Services for Higher Education." The example deploys an end-to-end medallion pipeline (Auto Loader + Whisper + Llama + Claude) for higher-ed advisor call quality analysis, exposed through three surfaces:

• A LangGraph agent served on Model Serving with UC function tools
• An Agent Bricks Knowledge Assistant grounded on a Vector Search index over call transcripts + rubric criteria
• A Genie Space over the enriched gold table

Included in this PR

New folder 2026-05-ai-advisory-higher-ed/ containing:

• 01_setup.py — UC catalog/schema/volume + 12 SQL UC functions
• 02_deploy.py — Auto Loader ingest, MLflow logging, agent deploy, KA Vector Search index, Genie setup notes
• 03_test.py — Pre/post-deploy validation tests
• agent.py — LangGraph agent (Claude + UC function tools + custom Python tools)
• README.md — prerequisites, run order, configuration
• LICENSE.md — Apache License 2.0

Why this belongs in databricks-blogposts

This repository is used for code examples shared through Databricks Blog posts, and this content is the companion code for an approved blog draft.

Validation

• All configuration is parameterized via notebook widgets (catalog, schema, volume, warehouse, endpoints); no hardcoded workspace IDs, user paths, or internal endpoints
• No secrets, tokens, customer data, or PII committed
• README documents prerequisites, costs, and run order
• Whisper attribution to OpenAI (MIT) included

Reviewer asks

• Public-repo readiness
• Folder naming and structure match repo conventions
• README clarity for an external first-time reader
• That the Vector Search + Knowledge Assistant setup steps in Stage 8 of 02_deploy.py match current product docs