Architecture

The underlying infrastructure that powers the Agent Party platform

Contents

System Architecture Overview

Agent Party is built on a modern, event-driven architecture designed for scalability, reliability, and extensibility. The system elegantly handles complex AI agent collaboration through a series of specialized layers.

Agent Party Architecture Diagram

Core Architectural Principles

Our architecture adheres to several key principles that enable seamless AI collaboration:

Event-Driven Communication

All system components communicate through standardized events, allowing for:

  • Loose coupling: Components can evolve independently
  • Asynchronous processing: Operations continue without blocking
  • Scale-out capability: Horizontal scaling for increased load
  • Replay and audit: Complete history of all system activities

Zero-Trust Security Model

Security is foundational in our architecture:

  • End-to-end encryption: All data is encrypted in transit and at rest
  • Fine-grained permissions: Access controls at the agent, team, and resource levels
  • Continuous verification: All operations are verified regardless of origin
  • Immutable audit log: Complete, tamper-proof record of all access and operations

Polyglot Persistence

The right storage technology for each data type:

  • Graph database: For complex agent relationships and capabilities
  • Object storage: For large artifacts and model weights
  • Key-value cache: For high-speed state and context management

Architecture Layers Explained

Agent Layer

The Agent Layer represents the collection of specialized AI agents within the system. Each agent is purpose-built for specific functions:

  • Doorman Agent: Controls access, authentication, and authorization
  • DJ Agent: Orchestrates workflows and coordinates agent activities
  • Bartender Agent: Provides the human interface for agent interactions

Agents communicate via standardized event protocols and maintain their own state while sharing context when needed.

# Example agent initialization
from agent_party import Agent, Capabilities

doorman = Agent(
    name="doorman",
    capabilities=[
        Capabilities.AUTHENTICATION, 
        Capabilities.AUTHORIZATION
    ],
    connection=kafka_connection
)

# Start the agent with its configuration
doorman.initialize(config={
    "security_level": "enterprise",
    "audit_log_enabled": True
})

Event Bus Layer

The Event Bus serves as the central nervous system of the platform:

  • Topics: Segregated channels for different event types (requests, responses, broadcasts)
  • Partitioning: Distributed processing for high throughput
  • Ordering: Guaranteed message ordering when required
  • Persistence: Configurable retention of event history

All system components connect to the event bus, making it the single source of coordination.

# Example Kafka topic configuration
topics:
  - name: agent.events
    partitions: 12
    replication_factor: 3
    retention.ms: 604800000  # 7 days
    
  - name: agent.requests
    partitions: 24
    replication_factor: 3
    retention.ms: 86400000   # 1 day
    
  - name: agent.responses
    partitions: 24
    replication_factor: 3
    retention.ms: 86400000   # 1 day

Core Layer

The Core Layer provides essential services that support agent operations:

Agent Registry

The Agent Registry maintains the catalog of agent templates and instances:

  • Stores agent capabilities, relationships, and compatibility data
  • Tracks resource usage and performance metrics
  • Enables discovery of agents based on capabilities
  • Uses Neo4j for relationship modeling and Redis for quick lookups

Event Processor

The Event Processor transforms and routes events between agents:

  • Applies transformations to match agent expectations
  • Enforces access control policies on event flow
  • Scales horizontally for high throughput scenarios
  • Built on Kafka Streams for stateful processing

State Manager

The State Manager handles agent and task state:

  • Maintains context for long-running operations
  • Enables checkpointing and resumability
  • Provides consistent state access across components
  • Uses Redis for high-speed access and consistency

Storage Layer

The Storage Layer provides persistent storage optimized for different data types:

Graph Database (Neo4j)

Stores complex relationships between:

  • Agents and their capabilities
  • Team structures and collaboration patterns
  • Task histories and dependencies

Object Storage (MinIO)

Manages large binary data:

  • Model weights and artifacts
  • Document and media content
  • Task inputs and outputs
  • Content for vector embedding

Cache (Redis)

Provides high-speed access to:

  • Active agent contexts
  • Session data
  • Frequently accessed configuration
  • Temporary processing results

Data Flow Patterns

Agent Collaboration Flow

  1. Request Initiation: A request enters through the Bartender agent
  2. Authentication: Doorman verifies the request’s authorization
  3. Orchestration: DJ determines which agents should handle the request
  4. Execution: Selected agents process their portions of the task
  5. Synthesis: Results are combined and returned to the requestor

Long-running Operation Flow

  1. Task Creation: System creates a persistent task record
  2. State Management: Task state is checkpointed throughout execution
  3. Progress Tracking: System provides real-time progress updates
  4. Result Handling: Completed results are stored and made available
  5. Notification: Interested parties are notified of completion

Deployment Architecture

Agent Party supports multiple deployment models to suit different organizational needs:

Single-Node Development

  • All components run on a single machine
  • Perfect for development and testing
  • Uses Docker Compose for simple setup/teardown

Kubernetes Production

  • Horizontally scalable deployment
  • Auto-scaling based on workload
  • High availability configuration
  • Helm charts for consistent deployment

Hybrid Cloud/On-Premise

  • Core components deployed on-premise
  • Burst capacity in cloud environments
  • Secure connection between environments
  • Data residency controls for sensitive information

Scalability Considerations

The architecture is designed to scale both horizontally and vertically:

  • Component Scaling: Each component scales independently
  • Kafka Partitioning: Distributes processing across nodes
  • Stateless Design: Most components maintain minimal state
  • Read Replicas: Distribute database read load
  • Caching Tiers: Multi-level caching reduces database pressure

Business Benefits

Cost Efficiency

  • Pay-as-you-grow: Scale resources according to actual usage
  • Resource Optimization: Components scale independently
  • Reduced Integration Costs: Standardized interfaces simplify onboarding new tools

Operational Excellence

  • High Availability: No single points of failure
  • Observable: Comprehensive metrics, logging, and tracing
  • Self-healing: Automated recovery from component failures
  • Upgradable: Rolling updates without system downtime

Future-Proof Investment

  • Modular Design: Components can be replaced individually
  • Standards-Based: Built on proven, open technologies
  • Vendor Neutral: Avoid lock-in to specific AI providers
  • Extensible: New agents and capabilities can be added seamlessly

Next Steps

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