Venue Onboarding Playbook
Take a venue from signed contract to live AI agent in one session — scrape, configure, optimize.
Why this playbook
Every venue on AGNT needs three things: structured data from its website, a configured agent personality, and optimized booking logic. Doing this manually takes 2-3 hours per venue and introduces inconsistencies across your fleet.
This playbook chains three production prompts that AGNT's own onboarding team runs for every new venue. The output of each step feeds the next — the intake pack.json becomes the hiring brief's knowledge base, and the personality config feeds the booking optimizer's tone calibration.
Run it once to onboard a venue. Run it weekly if you're scaling across multiple cities.
Prerequisites
- Claude Code installed with filesystem access
- AGNT MCP server connected (see /stack/mcp)
- Venue's website URL or a directory of scraped HTML/PDF files
- Access to AGNT's venue admin panel (for final deployment)
Input requirements
| Input | Type | Required | Description |
|---|---|---|---|
| Venue URL or scraped files | URL or directory path | Yes | The venue's public website. If the site is behind a paywall or login, provide pre-scraped HTML/PDF files in a local directory. |
| Venue category | string | Yes | Restaurant, bar, cafe, spa, coworking, hotel, or activity. Determines which fields the intake prompt extracts (e.g., "dress code" for restaurants, "check-in time" for hotels). |
| Booking platform | string | No | If the venue uses OpenTable, Resy, or a custom system, specify it so the booking optimizer can configure the right integration path. |
Step-by-step workflow
Scrape and structure venue data
Open promptStart here. This prompt crawls the venue's website (or reads your scraped files) and produces a structured knowledge pack in AGNT's venue format: JSON for hours, cuisines, and location; markdown for descriptions; and a facts table for policies.
The output is a draft — it marks every field with a confidence score and flags ambiguous data for human review. Never skip the review step.
Configure the venue's AI agent
Open promptFeed the pack.json from step 1 into this prompt. It generates a complete agent configuration: personality traits, tone of voice, knowledge boundaries, and escalation rules.
The hiring brief enforces AGNT's agent quality standards — it won't let you ship an agent that claims to know things outside its training data or that uses language inconsistent with the venue's brand.
Optimize booking and reservation handling
Open promptThe final step takes the agent config from step 2 and tunes the booking logic: confirmation messages, reminder timing, cancellation policy enforcement, and A2A handoff behavior if the venue uses ClawPulse.
This is where you configure whether the agent books directly or routes through a platform integration.
Expected outputs
pack.json
Structured venue knowledge pack with hours, cuisines, location, policies, and confidence scores.
Produced by step 1agent-config.yaml
Complete agent personality and knowledge configuration ready for deployment.
Produced by step 2booking-config.yaml
Booking flow configuration: confirmations, reminders, cancellation rules, A2A handoff settings.
Produced by step 3Tool requirements
- Claude Code with filesystem access
- AGNT MCP server
- Venue website URL or scraped files
Troubleshooting
Intake prompt returns low-confidence scores on most fields
Hiring brief rejects the pack.json as incomplete
Booking optimizer produces A2A config but venue doesn't use ClawPulse
Agent tone doesn't match the venue's brand
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Venue Onboarding Playbook 3 steps, 45-60 minutes. Take a venue from signed contract to live AI agent in one session — scrape, configure, optimize. https://agntdot.com/playbooks/venue-onboarding-playbook
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