# Smart Support -- Demo Script

## Overview

This script walks through a live demonstration of Smart Support, showcasing
multi-agent routing, human-in-the-loop interrupts, conversation replay,
and the analytics dashboard.

## Prerequisites

- Docker and Docker Compose installed
- API key for one of: Anthropic, OpenAI, or Google

## Setup (5 minutes)

### 1. Start the stack

```bash
cp .env.example .env
# Edit .env and add your ANTHROPIC_API_KEY (or other provider key)
docker compose up -d
```

Wait for all services to be healthy:

```bash
docker compose ps
# All services should show "healthy" or "running"
```

### 2. Seed demo data (optional)

```bash
docker compose exec backend python fixtures/demo_data.py
```

### 3. Open the app

Navigate to http://localhost in your browser.

---

## Demo Flow

### Scene 1: Basic Chat (2 minutes)

1. Open the Chat tab (default).
2. Send: **"What is the status of order 12345?"**
   - Observe the `tool_call` indicator appear in the sidebar (order_agent calling `get_order_status`).
   - The agent responds with order status.
3. Send: **"Can you cancel that order?"**
   - The system detects a write operation and shows an **Interrupt Prompt**.
   - Click **Approve** to confirm the cancellation.
   - The agent confirms cancellation.

Key points to highlight:
- Real-time token streaming (words appear as they are generated)
- Tool call visibility (transparency into what the agent is doing)
- Human-in-the-loop confirmation for write operations

### Scene 2: Multi-Agent Routing (2 minutes)

1. Start a new browser tab (new session) or clear session storage.
2. Send: **"I need to track my order AND request a refund for a previous order"**
   - The supervisor detects two intents: `order_agent` and `refund_agent`.
   - Both agents run in sequence.
   - Two interrupt prompts may appear if both operations are write-level.

Key points to highlight:
- Intent classification detecting multiple actions
- Automatic routing to appropriate specialist agents
- Sequential execution with confirmation gates

### Scene 3: Conversation Replay (2 minutes)

1. Click the **Replay** tab.
2. The conversation list shows all sessions, including the ones just conducted.
3. Click any thread to see the detailed step-by-step replay.
4. Expand a `tool_call` step to see the parameters and result.

Key points to highlight:
- Full audit trail of every agent action
- Expandable params/result for debugging
- Pagination for long conversations

### Scene 4: Analytics Dashboard (2 minutes)

1. Click the **Dashboard** tab.
2. Select the **7d** range.
3. Point out:
   - Total conversations and resolution rate
   - Agent usage breakdown (which agents handled how many messages)
   - Interrupt stats (approved vs. rejected vs. expired)
   - Cost and token usage

Key points to highlight:
- Operational visibility into agent performance
- Cost tracking per conversation/agent
- Resolution and escalation rates

### Scene 5: OpenAPI Import (2 minutes)

1. Click the **API Review** tab.
2. Paste the URL: `http://localhost:8000/openapi.json` (or the sample API URL)
3. Click **Import**.
4. Watch the job status update from `pending` to `processing` to `done`.
5. Review the classified endpoints table.
6. Edit the `access_type` for a sensitive endpoint (e.g., change `read` to `write`).
7. Click **Approve & Save**.

Key points to highlight:
- Zero-configuration discovery: paste a URL, get an agent
- AI-powered classification of endpoint sensitivity
- Human review gate before any endpoints go live

---

## Troubleshooting

**WebSocket shows "disconnected":**
- Check that the backend container is running: `docker compose logs backend`
- Verify port 8000 is not blocked

**No LLM responses:**
- Confirm your API key is set in `.env`
- Check backend logs: `docker compose logs backend`

**Database errors:**
- Run: `docker compose restart backend`
- If tables are missing: `docker compose exec backend python -c "import asyncio; from app.db import *; ..."`