One Supervisor, Many Modalities: Adaptive Tool Orchestration for Autonomous Queries

Abstract

We present an agentic AI framework for autonomous multimodal query processing that coordinates specialized tools across text, image, audio, video, and document modalities. A central Supervisor dynamically decomposes user queries, delegates subtasks to modality-appropriate tools (e.g., object detection, OCR, speech transcription), and synthesizes results through adaptive routing strategies rather than predetermined decision trees. For text-only queries, the framework uses learned routing via RouteLLM, while non-text paths use SLM-assisted modality decomposition. Evaluated on 2,847 queries across 15 task categories, our framework achieves 72% reduction in time-to-accurate-answer, 85% reduction in conversational rework, and 67% cost reduction compared to the matched hierarchical baseline while maintaining accuracy parity. These results demonstrate that intelligent centralized orchestration fundamentally improves multimodal AI deployment economics.

Figures (5)

• Figure 1: Architectural paradigm comparison showing the transition from scripted routing with brittle global restarts to Supervisor-driven adaptive orchestration with tool and model pools.
• Figure 2: Centralized orchestration architecture coordinating specialized tools across modalities through dynamic task decomposition and delegation.
• Figure 3: Modality-specific memory architecture with hierarchical layers and unified context scoring managed by the central orchestrator.
• Figure 4: Performance improvements across modalities showing consistent 65--77% TTA and 82--89% rework reduction.
• Figure 5: Cost analysis by tool category showing 62--85% reduction with Memory Tools achieving highest efficiency.