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Realtime multimodal orchestration coordinates AI agents and models to simultaneously process and respond to multiple data types—text, audio, images, and video. It enables smooth, context-aware interactions across complex multi-agent workflows, removing the single-modality constraint of traditional AI systems. 
Audio / Text / Image  →  Multimodal Model  →  Orchestrator  →  Agents  →  Output

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Key Capabilities

  • Richer context and understanding: Integrating multiple data types gives the system a deeper, more accurate picture of user needs and intent.
  • Improved accuracy and user experience: Cross-referencing modalities and maintaining conversation history produces more relevant responses and a seamless experience.
  • Scalability and flexibility: Orchestration frameworks scale across many agents and servers, supporting thousands of concurrent interactions without code changes.
  • Any input, any output: Multimodal AI handles text, images, audio, and other input types, and converts them into any output format.

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Multimodal Architecture

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Input Layer

Captures data from multiple sources—spoken queries (audio), written text, and uploaded images. A session manager handles WebSocket connections and detects the input modality on arrival.

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Native Multimodal Model

Modern realtime models such as OpenAI GPT-4o Realtime, Google Gemini Live, and Azure OpenAI Realtime API process audio and text natively, eliminating separate ASR → LLM → TTS pipelines. This preserves vocal nuances and reduces latency.

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Orchestration Layer

The app orchestrator plans, reasons, and delegates tasks to the right agents based on current context and user intent. It coordinates multi-agent workflows and maintains session state throughout the interaction.

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Task Execution and Coordination

Each agent runs its own task procedures, tools, and sub-agents. The orchestrator sequences tasks correctly and handles dynamic interactions—including mid-stream function calls—without interrupting the conversation flow.

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Output Layer

Delivers immediate streaming responses and adapts to new inputs or context changes in real time. Guardrails validate outputs to ensure reliability throughout the session.