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Architecture Overview

System Architecture

The AI Agents platform is built on a modular, scalable architecture that leverages IBM Orchestrate as its core orchestration engine. The system is designed to support multiple autonomous agents that can communicate, collaborate, and integrate with external services.

High-Level Architecture

graph TB
    subgraph "Presentation Layer"
        UI[Web Interface]
        API[REST API]
        CLI[Command Line Interface]
    end

    subgraph "Application Layer"
        AM[Agent Manager]
        WM[Workflow Manager]
        CM[Context Manager]
        MM[Message Manager]
    end

    subgraph "IBM Orchestrate Core"
        OE[Orchestration Engine]
        WE[Workflow Engine]
        IM[Integration Manager]
    end

    subgraph "Protocol Layer"
        MCP[Model Context Protocol]
        A2A[Agent-to-Agent Protocol]
    end

    subgraph "Agent Layer"
        A1[Agent Instance 1]
        A2[Agent Instance 2]
        AN[Agent Instance N]
    end

    subgraph "Data Layer"
        DB[(Database)]
        CACHE[(Cache)]
        QUEUE[(Message Queue)]
    end

    subgraph "External Services"
        LLM[LLM Services]
        EXT[External APIs]
        TOOLS[Tools & Services]
    end

    UI --> API
    CLI --> API
    API --> AM
    API --> WM

    AM --> OE
    WM --> WE
    CM --> OE
    MM --> OE

    OE --> MCP
    OE --> A2A
    WE --> IM

    MCP --> A1
    MCP --> A2
    MCP --> AN

    A2A --> A1
    A2A --> A2
    A2A --> AN

    A1 --> DB
    A2 --> DB
    AN --> DB

    MM --> QUEUE
    CM --> CACHE

    MCP --> LLM
    IM --> EXT
    A1 --> TOOLS
    A2 --> TOOLS
    AN --> TOOLS

    style OE fill:#0f62fe
    style MCP fill:#24a148
    style A2A fill:#ff832b

Core Components

1. Presentation Layer

The presentation layer provides multiple interfaces for interacting with the platform:

  • Web Interface: User-friendly dashboard for managing agents
  • REST API: Programmatic access for integrations
  • CLI: Command-line tools for developers and administrators

2. Application Layer

The application layer contains the business logic:

  • Agent Manager: Handles agent lifecycle (creation, deployment, monitoring)
  • Workflow Manager: Orchestrates complex multi-step processes
  • Context Manager: Maintains state and context across interactions
  • Message Manager: Routes and manages inter-agent communication

3. IBM Orchestrate Core

IBM Orchestrate provides the foundation:

  • Orchestration Engine: Coordinates agent activities and workflows
  • Workflow Engine: Executes predefined and dynamic workflows
  • Integration Manager: Connects to external systems and services

4. Protocol Layer

Two key protocols enable agent functionality:

  • Model Context Protocol (MCP): Standardizes AI model interactions
  • Agent-to-Agent Protocol (A2A): Facilitates inter-agent communication

5. Agent Layer

Individual agent instances that:

  • Execute specific tasks and responsibilities
  • Maintain their own state and context
  • Communicate with other agents
  • Integrate with external tools and services

6. Data Layer

Persistent storage and caching:

  • Database: Stores agent configurations, logs, and state
  • Cache: Improves performance for frequently accessed data
  • Message Queue: Ensures reliable asynchronous communication

Design Principles

Modularity

Each component is designed to be independent and replaceable, allowing for:

  • Easy maintenance and updates
  • Component-level scaling
  • Technology stack flexibility

Scalability

The architecture supports horizontal scaling:

  • Multiple agent instances can run concurrently
  • Load balancing across agent pools
  • Distributed processing capabilities

Reliability

Built-in resilience features:

  • Automatic retry mechanisms
  • Circuit breakers for external services
  • State persistence and recovery

Security

Security is integrated at every layer:

  • Authentication and authorization
  • Encrypted communication
  • Audit logging
  • Secure credential management

Communication Patterns

Synchronous Communication

Used for immediate responses:

  • REST API calls
  • Direct agent-to-agent messages
  • Real-time user interactions

Asynchronous Communication

Used for long-running tasks:

  • Message queue processing
  • Background job execution
  • Event-driven workflows

Deployment Models

Single Instance

Suitable for development and testing:

  • All components on one machine
  • Simplified configuration
  • Easy debugging

Distributed

Recommended for production:

  • Components across multiple servers
  • Load balancing and redundancy
  • High availability configuration

Cloud-Native

Optimized for cloud platforms:

  • Containerized deployments
  • Auto-scaling capabilities
  • Managed services integration

Next Steps