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  • Multi-agent collaboration

    Multi-agent collaboration organizes AI agents with different roles to collaborate to achieve one goal, sharing tasks and interacting to produce results.

    • Role sharing
    • Interaction Protocol
    • Goals-oriented execution

  • Tools and systems association

    Tool and system associations connect to external APIs, internal systems, databases, and more to enable AI to perform real-world action beyond simple analysis.

    • API Interworking
    • Internal System Linkage
    • Security Execution Environment

  • Lifecycle Management

    Lifecycle management continues to manage health and contextual information that occurs during the course of work, enabling consistent results and customized work.

    • Status Persistence
    • Customized execution
    • Error recovery
    • Real-time tracking

  • Agent Runtime

    Agent runtime refers to an environment where an AI agent actually operates and runs. Beyond just inference by a model (LLM), it provides all the 'execution base' needed for agents to interact with the outside world.

    • Real-time execution
    • Health Management
    • Resource Optimization
    • Isolation execution

  • Multimodal Agent Orchestration

    Multimodal agent orchestration refers to a chain of command that makes multiple multimodal agents (agents that understand different data formats) efficiently deployed and collaborated.

    • Intelligent deployment
    • Efficient cooperation
    • Central control

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