Distributed Agentic Architecture for Low-Latency Edge AI
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Solution Overview
Problem
Agentic AI systems face challenges in being deployed on edge devices due to memory and computing resource limitations, leading to latency issues and privacy concerns when using cloud-based systems.
Innovation Solution
A distributed agentic system is implemented, comprising multiple nodes including edge devices and virtual machines, with an agentic manager coordinating AI models and apps across these nodes, utilizing peer-to-peer or centralized communication channels, and managing concurrent user sessions and app invocations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If cloud-based systems are used for agentic AI, then computing resources and memory are sufficient, but latency increases and privacy concerns arise
Solution Approach 1:
The system segments the agentic AI architecture into multiple distributed nodes (edge devices and virtual machines) that can independently process AI tasks. Each node contains an agentic manager, AI models, and apps that work autonomously, eliminating the need for centralized cloud processing and reducing latency while maintaining sufficient computing resources locally.
Solution Approach 2:
The system transitions from a single centralized cloud dimension to a multi-dimensional distributed architecture where AI processing occurs across multiple spatial dimensions (different edge devices and VMs). This dimensional expansion allows parallel processing and reduces latency while preserving privacy by keeping data local.
2Power
If cloud-based systems are used for agentic AI, then computing power is adequate, but data security and privacy are compromised
Solution Approach 1:
The system extracts AI processing capabilities from centralized cloud infrastructure and places them directly on edge devices and virtual machines. This extraction removes sensitive user data from vulnerable transmission channels and centralized storage, eliminating privacy risks while preserving adequate computing power through local execution.
Solution Approach 2:
The system introduces virtual machines as intermediary layers between users and physical hardware. These VMs provide isolation and security boundaries that protect user data while enabling adequate computing power for AI tasks, thus resolving the conflict between privacy protection and computational capability.
3Loss of time
If AI models are deployed on edge devices, then latency is reduced, but memory and computing resources are limited
Solution Approach 1:
The system implements dynamic resource allocation where the agentic manager on each node intelligently manages limited memory and computing resources. AI models are loaded and executed dynamically based on task requirements, allowing the system to achieve low latency through local processing while efficiently utilizing constrained edge device resources.
4Speed
If AI models are deployed on edge devices, then real-time responsiveness is improved, but computing resources are insufficient
Solution Approach 1:
The system segments the AI processing workload across multiple edge device nodes, with each node having its own agentic manager and AI models. This segmentation enables distributed parallel processing that achieves real-time responsiveness while each individual node operates within its limited computing resource constraints.
Data Source
AI summary
A distributed agentic system includes multiple nodes that are communicatively connected. Each node is one of an edge device and a virtual machine (VM) operating on the edge device. Interaction peripherals are coupled to a subset of the nodes to receive user requests and output responses. In the distributed agentic system, an agentic manager receives a user request via one of the interaction peripherals. Based on the user request, the agentic manager sends a prompt to an artificial intelligence (AI) model managed by a model service. The agentic manager receives an action plan from the AI model, and calls at least one app according to the action plan to generate a response to the user request. The agentic manager, the AI model, the model service, and the at least one app are located on two or more of the nodes.