Deploying Foundation Model Powered Agent Services: A Survey (2412.13437v1)

Abstract: Foundation model (FM) powered agent services are regarded as a promising solution to develop intelligent and personalized applications for advancing toward AGI. To achieve high reliability and scalability in deploying these agent services, it is essential to collaboratively optimize computational and communication resources, thereby ensuring effective resource allocation and seamless service delivery. In pursuit of this vision, this paper proposes a unified framework aimed at providing a comprehensive survey on deploying FM-based agent services across heterogeneous devices, with the emphasis on the integration of model and resource optimization to establish a robust infrastructure for these services. Particularly, this paper begins with exploring various low-level optimization strategies during inference and studies approaches that enhance system scalability, such as parallelism techniques and resource scaling methods. The paper then discusses several prominent FMs and investigates research efforts focused on inference acceleration, including techniques such as model compression and token reduction. Moreover, the paper also investigates critical components for constructing agent services and highlights notable intelligent applications. Finally, the paper presents potential research directions for developing real-time agent services with high Quality of Service (QoS).

• The paper introduces a unified framework that integrates model and resource optimization for deploying efficient FM-powered agent services.
• It details low-level optimization and inference acceleration techniques, such as parallelism, model compression, and token reduction to enhance performance.
• The survey outlines future research directions for real-time AGI applications and multi-modal edge-cloud deployments with high QoS.

Deploying Foundation Model Powered Agent Services: A Survey

The paper "Deploying Foundation Model Powered Agent Services: A Survey" presents a thorough examination of the deployment techniques for Foundation Model (FM) powered agent services. These services highlight the transformative potential of FMs in advancing AGI by providing intelligent and personalized applications. The paper aims to establish a robust infrastructure for these services through a unified framework that integrates model and resource optimization.

Key areas of focus include low-level optimization strategies for inference, system scalability techniques like parallelism and resource scaling, and inference acceleration methods, including model compression and token reduction. The survey further elaborates on the essential components necessary for building agent services and showcases significant intelligent applications. Finally, it proposes future research directions toward developing real-time agent services with high Quality of Service (QoS).

Overview of Key Themes

1. Low-Level Optimization: The paper emphasizes the necessity of optimizing computational and communication resources to ensure high-performing FM applications. This includes strategies such as parallelism and resource scaling, which are critical for enhancing system scalability.
2. Inference Acceleration: Addressing the computational demands of FMs, the paper discusses several techniques for inference acceleration. Methods such as model compression and token reduction are highlighted as vital for promoting the widespread adoption of FMs by minimizing inference overheads and execution latency.
3. Construction of Intelligent Applications: The survey outlines the integration of various components — model layers, agent layers, and application layers — to develop sophisticated AI applications. It highlights the essential role of intelligent agents in extending the utility of FMs beyond traditional NLP tasks.

Implications and Future Directions

The paper suggests that the deployment of FM-powered agent services is pivotal for the next leap toward AGI. It implies that further advancements in resource and model optimization will be crucial in facilitating the widespread adoption of these services. Enhanced efficiency in real-world environments, especially across heterogeneous devices, will be a key determinant in realizing the full potential of FMs within various domains.

Future research is encouraged to explore more advanced methods for deploying multi-modal models and mixture-of-experts (MoE) frameworks on edge-cloud devices. Additionally, the development of specialized serving systems for agent architectures is suggested, which could streamline multi-agent collaboration, incorporate robust API calls, and ensure seamless knowledge retrieval.

Overall, this survey provides comprehensive insights into the existing landscape of FM-powered agents and serves as a catalyst for future innovations in deploying scalable, efficient, and intelligent applications in pursuit of AGI.