AI Agent System

Multi-Agent Swarm Intelligence

ParlayTix leverages a sophisticated multi-agent architecture with specialized AI agents working in harmony. From natural conversation to complex web automation, our swarm handles it all seamlessly.

6 Active Agents
2 AI Frameworks
100ms Avg Response

Beyond Simple Bots: True AI Collaboration

When we say "AI agents," we don't mean simple chatbots following scripts. We mean intelligent entities that can reason, plan, and collaborate to solve complex problems.

Imagine you're planning a trip to see your favorite team play in another city. A simple bot would just search for tickets. Our agent swarm does so much more:

  • Understands the full context of your trip and preferences
  • Considers travel times and nearby hotel availability
  • Checks multiple venues and alternative dates simultaneously
  • Monitors real-time price trends and predicts future changes
  • Factors in weather forecasts for outdoor venues

This isn't programmed behavior—it's emergent intelligence from multiple specialized agents working together.

"Each agent is like a specialist on a medical team—individually brilliant, but together they achieve outcomes impossible for any one alone."

Two-Layer AI Architecture

Frontend Agent

Powered by ElizaOS, this conversational layer handles all user interactions across platforms.

  • Natural language understanding
  • Multi-platform support (Web, Telegram, Twitter)
  • Context-aware conversations
  • Real-time response streaming

Agent Swarm

OpenAI-powered backend orchestration that processes complex tasks without user interaction.

  • Task decomposition and planning
  • Multi-agent coordination
  • Intelligent routing and caching
  • Async workflow orchestration

The ElizaOS Advantage

Our Frontend Agent, built on the ElizaOS framework, represents a breakthrough in conversational AI. Unlike traditional chatbots that lose context between messages, ElizaOS maintains rich conversation state, understanding not just what you're saying, but what you meant three messages ago.

This framework allows Pepper to engage in natural, flowing conversations across multiple platforms simultaneously. Whether you're chatting on our website, sending a Telegram message, or tweeting at us, Pepper remembers your preferences, your past interactions, and your ongoing requests. It's like having a personal ticket concierge who actually knows you.

The real power comes from ElizaOS's ability to seamlessly hand off complex tasks to our Agent Swarm while maintaining the conversation. You might be casually chatting about upcoming concerts while the swarm is orchestrating a complex multi-site search in the background. When results are ready, Pepper weaves them naturally into the conversation, making the technical complexity invisible.

Specialized Agent Roles

Search Agent

Intelligently queries multiple ticket marketplaces, comparing prices and availability in real-time.

Crawler Agent

Automates browser interactions using Playwright, navigating complex sites with anti-bot evasion.

Analysis Agent

Processes scraped data, identifies patterns, and provides intelligent recommendations.

Router Agent

Decides optimal execution paths, choosing between cached data and fresh crawls.

Action Agent

Executes transactions, manages bookings, and handles payment processing securely.

Monitor Agent

Tracks price changes, sends alerts, and maintains watchlists for users.

Agents You Don't See

Beyond the visible agents that directly serve your requests, our swarm includes specialized agents that work behind the scenes. The Cache Optimizer Agent continuously analyzes request patterns to predict what data you'll need next. The Fraud Detection Agent monitors for suspicious ticket listings and validates seller credibility. These invisible guardians ensure that every recommendation from Pepper is not just relevant, but safe and reliable.

The coordination between agents happens through what we call "semantic message passing"—agents don't just exchange data, they share understanding. When the Search Agent finds tickets, it doesn't just pass prices and seats to the Analysis Agent. It shares context: "These are premium seats, but they're in a section known for obstructed views, and the price is 20% above market rate." This rich communication enables nuanced decision-making that feels almost human.

Example Semantic Message:

{
  "agent": "SearchAgent",
  "tickets_found": 12,
  "analysis": {
    "venue_reputation": "excellent",
    "section_quality": "premium - unobstructed views",
    "price_comparison": "20% above market average",
    "price_trend": "decreasing - dropped $15 last 48h",
    "urgency": "low - event in 3 weeks",
    "similar_events": "2 other games same week"
  },
  "recommendation": {
    "action": "WAIT",
    "reason": "Prices likely to drop further",
    "target_price": "$175-$190",
    "confidence": 0.92
  },
  "metadata": {
    "processing_time_ms": 342,
    "sources_checked": 8,
    "cache_hit": false
  }
}

Intelligent Request Processing

1

User Query Reception

Frontend Agent receives and understands user intent through natural language processing

2

Task Delegation

Complex tasks are delegated to the Agent Swarm for parallel processing

3

Swarm Orchestration

Multiple specialized agents work together to gather, analyze, and process data

4

Result Synthesis

Results are aggregated, formatted, and delivered back through the conversational interface

Powered By Advanced Technology

ElizaOS

Conversational AI Framework

OpenAI

GPT-4 Agent Orchestration

Playwright

Browser Automation

RabbitMQ

Message Queue System

Key Capabilities

Parallel Processing

Multiple agents work simultaneously for faster results

Self-Healing

Automatic retry and fallback strategies

Context Awareness

Maintains conversation history and user preferences

Anti-Detection

Advanced techniques to bypass bot detection

Smart Caching

Intelligent data caching for instant responses

Scalable Architecture

Handles thousands of concurrent requests

The Future of Agent Collaboration

We're just scratching the surface of what's possible with multi-agent systems. Our roadmap includes agents that can learn from collective user behavior, predict event popularity before tickets go on sale, and even negotiate with venue systems on your behalf.

Imagine agents that understand not just what tickets you want, but why you want them. Agents that can spot patterns across millions of transactions to identify scalpers before they affect prices. Agents that collaborate with venue partners to create dynamic pricing that benefits both fans and artists.

As we expand our agent swarm, each new capability doesn't just add a feature—it multiplies the intelligence of the entire system. When agents can freely collaborate, share knowledge, and learn from each other, we move beyond automation into true artificial intelligence.

Coming Soon:

Predictive Agents

Anticipate ticket drops before official announcements

Learning Agents

Adapt to your preferences and buying patterns

Negotiation Agents

Secure group discounts and package deals

Social Agents

Coordinate purchases with friends in real-time

This is the future we're building at ParlayTix: not just smarter ticket buying, but a glimpse into how AI agents will transform every aspect of commerce. Join us on this journey.

Experience AI-Powered Ticketing

Our multi-agent system works tirelessly to find you the best tickets, monitor prices, and execute trades automatically.