[Enhancement] Leverage AppKit Genie Plugin and Agent Plugin for live in-app Agent & Genie integration

[varunrao]

Enhancement: AppKit for Agent and Genie Connection

Summary

The workshop's Data Intelligence chapter (Steps 17-19, 25) guides participants through building Genie Spaces and multi-agent orchestrators, but the workshop app itself has no live integration with either. All Agent and Genie steps are delivered as copy-paste prompts for an external IDE -- participants build these assets externally and verify them by opening workspace URLs.

Databricks AppKit now ships a Genie Plugin and has an Agent Plugin in active development that could bring live Genie and Agent interactions directly into the workshop UI. Combined with the Genie Conversation APIs (Public Preview), the workshop can evolve from a prompt-generation tool to an interactive platform where participants build, test, and interact with their Genie Spaces and Agents without leaving the app.

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1. Current State: Prompt-Only Agent & Genie Steps

The workshop has 5 steps involving Agents and Genie, all in the "Data Intelligence" chapter:

Step	Section Tag	What It Does	How It Works Today
17	genie_space	Build semantic layer (TVFs, Metric Views) + Genie Space	Generates a large prompt referencing semantic-layer/ skill files; participant copies to IDE
18	agent_framework	Build multi-agent orchestrator with Genie integration	References agentic-framework/agents/multi-agent-build-prompt.md; participant copies to IDE
19	wire_ui_agent	Wire app frontend to agent serving endpoint	References agent-ui-wiring-prompt.md; participant copies to IDE
25	optimize_genie	Benchmark and optimize Genie Space for production	References 05-genie-optimization-orchestrator/SKILL.md; hidden by default
Accelerator	genie-accelerator	Filtered workshop path focusing on Genie steps	Hides non-Genie steps (18, 19, 16) from the DI section

What the backend does today:

• routes.py has agent-format payload compatibility for Foundation Model serving endpoints (fallback JSON shapes for input vs messages), but no Genie Conversation API calls and no embedded Genie or Agent chat
• Verification links (verificationLinks.ts) point to {workspace_url}genie?o=... -- participants leave the app to verify
• ServicePopover.tsx shows genieSpaces and agents as service blurbs with doc links, but no interactive functionality
• The skill tree (skillTreeMapping.ts) maps steps 17-19 to orchestrator/worker skill hierarchies for prompt generation -- pure content, no API integration

There are zero API calls to:

• Genie Conversation API (POST /api/2.0/genie/spaces/{space_id}/conversations)
• Genie Management API (POST /api/2.0/genie/spaces)
• Agent serving endpoints (beyond the generic Foundation Model payload fallback)
• Agent Framework APIs

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2. What AppKit Offers

2a. Genie Plugin (Shipped -- v0.21.0)

AppKit's Genie Plugin provides a conversational AI interface powered by Databricks AI/BI Genie:

• Natural-language data querying against curated Genie Spaces
• Automatic chart inference and visualization from query results
• Stateful multi-turn conversations
• Embeddable React components

This could be integrated into the workshop so that after Step 17 (where participants create their Genie Space), they can immediately test it inside the workshop app via an embedded Genie chat panel -- instead of navigating to the workspace URL.

2b. Agent Plugin (In Active Development)

AppKit has multiple active PRs for an Agent Plugin:

PR	Title	Status	Date
#138	AgentPlugin + ChatUIPlugin for LangChain/LangGraph agents (POC)	Open	Feb 26, 2026
#166	AgentPlugin for LangChain/LangGraph agents	Open (47 comments)	Mar 9, 2026
#176	Tracing integration for agent plugin	Open	Mar 11, 2026
#217	PoC agent plugin (latest)	Draft	Mar 26, 2026

Once shipped, this plugin would allow the workshop to embed an agent chat UI so participants can interact with the multi-agent orchestrator they built in Step 18 -- directly from the workshop app, with tracing and observability built in.

2c. Genie Conversation APIs (Public Preview)

The Genie Conversation APIs enable:

• Programmatic Genie Space creation (POST /api/2.0/genie/spaces) -- the workshop could auto-create a Genie Space from the participant's Gold tables as part of Step 17
• Stateful conversations (POST /api/2.0/genie/spaces/{space_id}/conversations) -- embedded multi-turn chat
• Space management -- update instructions, add benchmark questions, configure data sources via API
• Validation -- run benchmark questions programmatically to verify Genie Space quality (Step 25)

2d. Related AppKit Plugins

Plugin	Relevance
Vector Search (PR #200)	Could complement agent RAG workflows built in Step 18
Files Plugin (shipped)	UC Volumes file management for agent artifacts
Analytics Plugin (shipped)	Type-safe SQL queries that agents could reference

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3. Proposed Enhancements

3a. Embedded Genie Chat (Post-Step 17)

After participants complete Step 17 (Genie Space creation), show an embedded Genie conversational panel in the workshop UI:

• Use AppKit's Genie Plugin to render a chat interface connected to the participant's newly created Genie Space
• Participant can test natural-language queries against their data immediately
• Results show with automatic chart inference -- participants see the value of their semantic layer without leaving the app
• Replace the current "Open Genie Space" verification link with this live panel

3b. Programmatic Genie Space Creation (Step 17 Automation)

Use the Genie Conversation API to automate parts of Step 17:

• After the participant's Gold tables are created (Steps 11-14), the workshop backend can auto-discover available tables via Unity Catalog
• Pre-populate a Genie Space configuration (tables, sample questions, instructions) based on the participant's use case
• Call POST /api/2.0/genie/spaces to create the space programmatically
• Participant refines via the embedded Genie chat rather than writing all the config by hand in the IDE

3c. Embedded Agent Chat (Post-Step 18/19)

After participants deploy their agent (Steps 18-19), embed an agent chat panel:

• Use AppKit's Agent Plugin (once shipped) to render a chat UI connected to the participant's agent serving endpoint
• Participant can test the multi-agent orchestrator directly: ask questions that route through the Genie integration, LLM rewrite, and web search fallback
• Show agent tracing (via AppKit's tracing integration PR #176) so participants can see how the orchestrator routes queries

3d. Genie Benchmark Automation (Step 25)

Use the Genie API to automate the optimization loop in Step 25:

• Programmatically run benchmark questions against the Genie Space
• Display accuracy metrics in the workshop UI
• Suggest improvements (additional instructions, example SQL, TVF adjustments) based on benchmark results
• Currently this step is hidden (step_enabled = FALSE) -- with live API integration, it becomes practical to enable by default

3e. Genie Accelerator Upgrade

The genie-accelerator workshop level currently just filters steps. With AppKit Genie Plugin integration, it could become a fully interactive Genie experience:

• Guided Genie Space creation with live preview
• Iterative refinement loop: ask question -> see result -> add instruction -> re-test
• Benchmark-driven optimization with accuracy scores
• All within the workshop app, zero IDE switching

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4. What Needs to Change

Component	Current	Proposed
Backend (routes.py)	No Genie API calls; agent-format payload fallback only	Add Genie Conversation API routes; add agent endpoint proxy routes
Frontend (WorkflowDiagram.tsx)	Steps 17-19 render as prompt generators	Add embedded Genie/Agent chat panels after step completion
Verification (verificationLinks.ts)	Links to {workspace_url}genie?o=...	In-app Genie panel replaces external link
Services (ServicePopover.tsx)	Static blurbs for genieSpaces and agents	Live status indicators (space created, agent deployed)
Config (app.yaml)	No Genie Space ID or Agent endpoint env vars	Add GENIE_SPACE_ID, AGENT_SERVING_ENDPOINT per-session vars
Session state (Lakebase)	No Genie/Agent metadata stored	Store genie_space_id, agent_endpoint_name in session
Step 25 (optimize_genie)	Hidden by default, prompt-only	Enabled by default with live benchmark execution
Skill tree (skillTreeMapping.ts)	Content references only	Add "Test in App" action nodes that trigger embedded panels
Deployment	No AppKit dependency	Add AppKit Genie Plugin as frontend dependency; Agent Plugin when available

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5. Proposed Implementation Phases

Phase 1: Genie Conversation API Backend

• Add Genie API proxy routes to routes.py (create space, start conversation, send message, get results)
• Store genie_space_id in session metadata (Lakebase sessions table)
• Add GENIE_WAREHOUSE_ID to app.yaml env vars

Phase 2: Embedded Genie Chat UI

• Integrate AppKit Genie Plugin (or build custom React component using Genie API)
• Add Genie chat panel to Step 17 completion view
• Replace genie_space verification link with in-app panel

Phase 3: Agent Plugin Integration

• Track AppKit Agent Plugin development (PRs #166, #217)
• When shipped: integrate Agent Plugin for embedded agent chat after Steps 18-19
• Add agent endpoint metadata to session state

Phase 4: Automated Benchmarking (Step 25)

• Use Genie API to run benchmarks programmatically
• Display accuracy metrics in the workshop UI
• Enable Step 25 by default with live functionality

Phase 5: Genie Accelerator Interactive Mode

• Redesign genie-accelerator workshop level as a guided, interactive Genie experience
• Live create -> test -> refine -> benchmark loop entirely in-app

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6. Dependencies and Risks

• AppKit Genie Plugin: Shipped (v0.21.0) -- ready for integration
• AppKit Agent Plugin: In development (PRs open) -- Phase 3 depends on this shipping; can be deferred
• Genie Conversation API: Public Preview -- stable enough for workshop use, but API may change
• Session architecture: Currently sessions store step prompts as JSON; adding Genie/Agent metadata requires schema evolution
• Auth flow: Genie API requires the participant's credentials (not the workshop app's SP) to access their Genie Space -- may need OAuth delegation or a different auth pattern
• AppKit migration: If Issue [PFR] Integrate Databricks AppKit SDK to replace custom React + FastAPI + Lakebase stack #1 (full AppKit migration) proceeds first, Genie/Agent plugins integrate naturally; if this enhancement is done standalone, it adds a hybrid AppKit + FastAPI architecture

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References

• AppKit Genie Plugin: https://databricks.github.io/appkit/
• AppKit Agent Plugin PRs: #138, #166, #176, #217
• Genie Conversation API: https://docs.databricks.com/aws/en/genie/conversation-api
• Genie blog post: https://www.databricks.com/blog/genie-conversation-apis-public-preview
• Current Genie step: src/constants/workflowSections.ts (steps 17, 22, 25)
• Current Agent steps: src/constants/workflowSections.ts (steps 18, 19)
• Genie Accelerator: src/constants/workflowSections.ts (genie-accelerator level filtering)
• Agent seed prompts: db/lakebase/dml_seed/02_seed_section_input_prompts.sql (agent_framework, wire_ui_agent)
• Skill tree mapping: src/constants/skillTreeMapping.ts (genie_space, agent_framework, wire_ui_agent)
• Verification links: src/constants/verificationLinks.ts (genie_space, optimize_genie)
• Service popovers: src/components/ServicePopover.tsx (genieSpaces, agents)
• Related issues: [PFR] Integrate Databricks AppKit SDK to replace custom React + FastAPI + Lakebase stack #1 (AppKit SDK migration), [PFR] Add Genie Code support as a workshop coding assistant target #2 (Genie Code support)