Building Beet for the Agent-Driven Future¶

What Makes a Platform Agent-Ready?¶

Following Karpathy's vision, agent-ready platforms enable AI systems to: - Navigate and understand all features programmatically - Execute actions on behalf of users - Learn and adapt from interactions - Collaborate with other agents

Beet's Agent Architecture¶

Universal API Layer¶

Every Beet feature exposes semantic APIs:

GET /api/v1/movies/search
Parameters:
- query: "Telugu movies this weekend"
- location: auto-detected or specified
- preferences: user profile or explicit

Response:
- Structured movie data
- Availability status
- Related recommendations
- Action endpoints

Semantic Understanding¶

APIs understand intent, not just keywords: - "Find something fun for date night" → Movies + Restaurants - "Plan my mom's birthday" → Events + Restaurants + Gift ideas - "I'm bored this weekend" → Personalized entertainment options

Agent Authorization Framework¶

Graduated Permissions: 1. Read-Only: Browse and recommend 2. Assisted Booking: Require confirmation 3. Autonomous Actions: Pre-authorized spending limits 4. Full Agency: Complete entertainment planning

Smart Limits: - Spending caps by category - Time-based restrictions - Location boundaries - Social permissions

Agent Capabilities by Feature¶

Entertainment Planning Agent¶

What it can do: - Monitor movie releases matching preferences - Track ticket availability and pricing - Coordinate group bookings - Optimize for rewards and discounts

Example Flow: 1. Agent detects new Telugu movie release 2. Checks user's typical movie-going patterns 3. Finds optimal showtime based on calendar 4. Identifies nearby Indian restaurants 5. Proposes complete evening plan 6. Books upon approval (or automatically if authorized)

Social Coordination Agent¶

Capabilities: - Analyze friend group availability - Suggest group-friendly activities - Handle complex scheduling - Manage group payments

Smart Features: - "Find time when 80% of group is free" - "Book only if at least 4 friends confirm" - "Split costs based on past patterns"

Rewards Optimization Agent¶

Functions: - Track all earning opportunities - Maximize point accumulation - Smart redemption timing - Cross-partner optimization

Intelligence: - "Use Beet Pay at this restaurant for 3x points" - "Save points for Diwali - better redemption value" - "Complete 2 more activities for bonus rewards"

Cultural Calendar Agent¶

Responsibilities: - Track cultural and religious events - Proactive booking suggestions - Community event coordination - Festival preparation assistance

Contextual Understanding: - Knows Navratri dates and suggests Garba events - Books restaurants for Iftar during Ramadan - Coordinates Diwali celebration venues

Agent Interoperability¶

Cross-Platform Integration¶

Beet agents can work with: - Calendar Apps: Sync entertainment plans - Payment Platforms: Coordinate transactions - Social Networks: Event invitations - Transportation: Uber/Lyft for events - Communication: WhatsApp for group coordination

Agent-to-Agent Protocol¶

Beet agents can negotiate with: - Restaurant Agents: Table availability and pricing - Theater Agents: Group booking discounts - Event Agents: Exclusive access or deals - Payment Agents: Optimal payment routing

Privacy and Control¶

User Sovereignty¶

• Transparent Actions: See what agents are doing
• Granular Controls: Specific permissions by feature
• Activity Logs: Complete audit trail
• Instant Revocation: One-click to disable

Privacy-Preserving AI¶

• Local Preference Processing: Sensitive data stays on device
• Federated Learning: Improve without sharing personal data
• Differential Privacy: Aggregate insights without individual exposure
• Zero-Knowledge Proofs: Verify eligibility without revealing details

Implementation Roadmap¶

Phase 1: Foundation (Current)¶

• RESTful APIs for all features
• Basic authentication and authorization
• Simple rule-based automation

Phase 2: Intelligence (6 months)¶

• Natural language API interfaces
• Learning user preferences
• Basic autonomous actions
• Agent SDK release

Phase 3: Ecosystem (12 months)¶

• Full agent marketplace
• Cross-platform negotiations
• Complex multi-step workflows
• Community-built agents

Success Metrics¶

• API Coverage: 100% of features accessible via API
• Agent Adoption: 30% of users enable at least one agent
• Automation Rate: 15% of bookings via agents
• Developer Ecosystem: 50+ third-party agents

Developer Resources¶

Agent SDK¶

• Natural language processing helpers
• Authentication workflows
• Payment integration
• Notification management

Testing Environment¶

• Sandbox with fake money/points
• Simulated user populations
• Time manipulation for testing
• Event simulation tools

Documentation¶

• API reference with examples
• Agent design patterns
• Security best practices
• Performance guidelines

Document Owner: Platform Architecture Team Next Review: Q1 2026