One Supervisor, Many Modalities: Adaptive Tool Orchestration for Autonomous Queries
arXiv:2603.11545v1 Announce Type: new 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 eco
arXiv:2603.11545v1 Announce Type: new 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.
Executive Summary
This article presents an agentic AI framework for autonomous multimodal query processing, which dynamically decomposes user queries and delegates subtasks to modality-appropriate tools, achieving significant reductions in time-to-answer, conversational rework, and costs. The framework utilizes learned routing for text-only queries and SLM-assisted modality decomposition for non-text paths. The results demonstrate the potential of intelligent centralized orchestration to improve multimodal AI deployment economics. However, the article's limitations lie in its reliance on pre-existing tools and datasets, as well as the need for further exploration of its scalability and adaptability. Despite these concerns, the framework's performance and potential applications make it a valuable contribution to the field of AI research.
Key Points
- ▸ The article presents an agentic AI framework for autonomous multimodal query processing.
- ▸ The framework dynamically decomposes user queries and delegates subtasks to modality-appropriate tools.
- ▸ The results demonstrate a 72% reduction in time-to-accurate-answer, 85% reduction in conversational rework, and 67% cost reduction compared to the matched hierarchical baseline.
Merits
Strength in Multimodal Query Processing
The framework's ability to dynamically decompose user queries and delegate subtasks to modality-appropriate tools represents a significant advancement in multimodal query processing.
Improved Deployment Economics
The framework's performance demonstrates the potential for intelligent centralized orchestration to improve multimodal AI deployment economics.
Demerits
Limitation in Tool Reliance
The framework's reliance on pre-existing tools and datasets may limit its adaptability and scalability in real-world applications.
Need for Further Exploration
The article's limitations highlight the need for further exploration of the framework's performance in various scenarios and its potential for future development.
Expert Commentary
The article presents a significant advancement in multimodal query processing, leveraging the power of intelligent centralized orchestration to improve AI deployment economics. However, the framework's reliance on pre-existing tools and datasets may limit its adaptability and scalability. Further exploration of the framework's performance in various scenarios and its potential for future development is necessary to fully realize its potential. Additionally, the article's findings highlight the need for a nuanced discussion of the policy and regulatory implications of intelligent centralized orchestration.
Recommendations
- ✓ Future research should focus on exploring the framework's performance in various scenarios and its potential for future development.
- ✓ Policy and regulatory frameworks governing AI development and deployment should take into account the potential implications of intelligent centralized orchestration on AI deployment economics.