Abstract
Modern AI systems are rapidly evolving from monolithic models into agentic, service-oriented architectures, where multiple agents collaborate to execute complex workflows composed of specialized services such as planning, reasoning, retrieval, tool execution, validation, and orchestration. These workflows increasingly span multiple agents, heterogeneous runtimes, and multiple providers, communicating through agent-to-agent (A2A) protocols.
While Quality of Service (QoS) mechanisms are well established in networking and distributed systems, existing approaches are insufficient for agentic AI systems. Current solutions operate at coarse granularities such as network flows, inference requests, or entire workflows and rely on static configuration. They lack awareness of service execution semantics, do not adapt dynamically to changing agent behavior, and fail to provide consistent end-to-end QoS across semantic, transport, and execution layers, particularly in multi-provider environments.
The proposal introduces a service-aware, closed-loop QoS control system specifically designed for multi-agent, multi-provider AI systems. The core novelty lies in treating services executed within agents as the primary unit of QoS, rather than agents, models, or network flows.
The system dynamically infers QoS requirements from agent execution context, including service type, workflow position, dependency structure, and historical behavior. These inferred service-level QoS profiles are then propagated as portable QoS intent metadata across agent-to-agent communication, preserving QoS requirements even as workflows traverse agent and provider boundaries.
A key novelty of the proposal is the coordinated, cross-layer enforcement of QoS using a unified service-level policy model. The same inferred QoS intent is applied coherently across:
- the semantic layer (workflow ordering and execution paths),
- the execution layer (service scheduling, resource allocation, model selection), and
- the transport layer (prioritized agent-to-agent communication).
The proposed system further incorporates a closed-loop adaptation mechanism, continuously monitoring service-level telemetry and dynamically adjusting priorities, resource budgets, and routing decisions. This enables redistribution of unused capacity, protection of latency-critical services, and graceful degradation under overload without manual tuning or static provisioning.
Unlike existing methods that addresses QoS in isolation at the network, inference, or workflow level, the proposed approach delivers end-to-end, service-level QoS guarantees driven by execution semantics and maintained across distributed, federated AI systems. It explicitly supports hybrid SLM/LLM architectures, multi-tenant environments, and multi-vendor deployments.
The proposal represents a natural extension of its long-standing leadership in QoS, networking, security, and observability into the emerging domain of enterprise-scale agentic AI, enabling predictable performance, efficient resource utilization, and differentiated AI-driven solutions across networking, security, collaboration, and hybrid cloud platforms.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
M M, Niranjan, "Service-Aware Quality of Service Control for Multi-Agent, Multi-Provider AI Systems", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/9844
Illustrative Example