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Architecture Overview

Pixell is an AI-to-AI integration platform that enables seamless communication between AI agents. This document provides a high-level overview of the system architecture and how all components work together.

System Components​

Pixell consists of four main components that work together to create a complete agent ecosystem:

🧰 PAK (Pixell Agent Kit)​

Purpose: Development tools and SDK for building agent applications

Key Features:

  • CLI for project management (pak init, pak build, pak dev)
  • SDK for agent logic and UI development
  • APKG packaging system
  • Local testing environment

Developer Workflow:

  1. pak init - Create new agent project
  2. Write agent logic in agent/ directory
  3. Create UI components in ui/ directory
  4. pak build - Package into APKG file
  5. pak dev - Test locally

⚙️ PAR (Pixell Agent Runtime)​

Purpose: Execution environment for running agent applications

Key Features:

  • Loads and executes APKG packages
  • Provides REST and gRPC APIs
  • Manages agent lifecycle (start, stop, restart)
  • Handles resource isolation and security

Runtime Capabilities:

  • Multi-agent execution
  • Resource management
  • Health monitoring
  • Log aggregation

☁️ PAC (Pixell Agent Cloud)​

Purpose: Cloud orchestration and deployment platform

Key Features:

  • APKG deployment and management
  • Auto-scaling based on demand
  • Load balancing and routing
  • Monitoring and observability

Deployment Flow:

  1. Upload APKG to PAC
  2. PAC provisions PAR instances
  3. Load balancer routes traffic
  4. Auto-scaling adjusts capacity

💡 PAF-Core (Pixell Agent Framework Core)​

Purpose: Core framework providing shared services and protocols

Key Features:

  • Intent-based routing system
  • Agent-to-Agent (A2A) communication protocol
  • Message routing and discovery
  • Security and authentication

Data Flow Architecture​

Agent Package (APKG) Structure​

APKG is a standardized package format for distributing agent applications:

my-agent.apkg
├── manifest.json          # Agent metadata and configuration
├── agent/
│   ├── index.js          # Main agent logic
│   ├── handlers/         # Intent handlers
│   └── utils/            # Utility functions
├── ui/
│   ├── components/       # React components
│   ├── pages/           # UI pages
│   └── styles/          # CSS/styling
├── assets/              # Static assets
└── package.json         # Dependencies

Manifest.json Schema​

{
  "name": "my-agent",
  "version": "1.0.0",
  "description": "My awesome agent",
  "author": "Developer Name",
  "intents": [
    {
      "name": "greet",
      "description": "Greet the user",
      "parameters": {
        "name": "string"
      }
    }
  ],
  "ui": {
    "entry": "ui/index.jsx",
    "routes": [
      {
        "path": "/",
        "component": "HomePage"
      }
    ]
  },
  "runtime": {
    "node": "18.x",
    "memory": "512MB",
    "timeout": "30s"
  }
}

Agent-to-Agent (A2A) Protocol​

The A2A protocol enables agents to discover and communicate with each other:

A2A Message Format​

{
  "id": "msg-123",
  "from": "agent-a",
  "to": "agent-b",
  "intent": "process-data",
  "parameters": {
    "data": "...",
    "format": "json"
  },
  "context": {
    "sessionId": "session-456",
    "userId": "user-789"
  },
  "timestamp": "2024-01-01T00:00:00Z"
}

Security Architecture​

Security Features​

  • API Authentication: JWT tokens and API keys
  • Agent Isolation: Sandboxed execution environments
  • Network Security: VPC isolation and firewall rules
  • Data Encryption: TLS in transit, AES-256 at rest
  • Access Control: Role-based permissions (RBAC)

Scalability Architecture​

Scaling Strategies​

  • Horizontal Scaling: Add more PAR instances
  • Auto-scaling: Based on CPU, memory, and request metrics
  • Load Balancing: Distribute traffic across instances
  • Caching: Redis for session and data caching
  • Database Sharding: Partition data across multiple databases

Integration Points​

External Integrations​

Supported Integrations​

  • LLM Providers: OpenAI, Anthropic, Cohere, local models
  • Databases: PostgreSQL, MongoDB, Redis, DynamoDB
  • Storage: AWS S3, Google Cloud Storage, Azure Blob
  • APIs: REST, GraphQL, gRPC, WebSocket
  • Frameworks: LangChain, LangGraph, AutoGen

Development Workflow​

Development Phases​

  1. Development: Write agent logic and UI
  2. Testing: Use pak dev for local testing
  3. Building: Create APKG with pak build
  4. Deployment: Deploy to PAC with pixell deploy
  5. Monitoring: Track performance and errors
  6. Iteration: Debug and improve based on feedback

Performance Characteristics​

Latency Targets​

  • Agent Response: < 200ms for simple intents
  • A2A Communication: < 100ms between agents
  • UI Rendering: < 500ms for complex interfaces
  • Cold Start: < 2s for new agent instances

Throughput Targets​

  • Concurrent Users: 10,000+ per agent
  • Requests per Second: 1,000+ per PAR instance
  • A2A Messages: 10,000+ per second
  • Agent Deployments: 100+ per minute

Monitoring and Observability​

Monitoring Stack​

  • Metrics: Prometheus + Grafana
  • Logs: ELK Stack (Elasticsearch, Logstash, Kibana)
  • Traces: Jaeger or Zipkin
  • Alerting: AlertManager with Slack/email notifications

Next Steps​

Additional Resources​