arXiv:2605.27744v1 Announce Type: new Abstract: Multi-agent LLM systems have become the dominant production workload, but the serving stack was not built for them. The agent framework above knows agent identities, role, schemas, and dispatch structure but never sees an engine-level event; the serving engine below sees every event but knows nothing about agents. A surprising number of cross-cutting policies depend on both: prefix caching, batch shaping, speculative execution, fairness, tool-result memoization, safety enforcement, and more. Each lives in the seam between the two layers and is cu
The proliferation of multi-agent LLM systems in production necessitates a new runtime paradigm to manage their complex, distributed execution and unique policy requirements.
This paper highlights a critical architectural gap in current LLM serving infrastructure, directly addressing the operational challenges and performance bottlenecks of advanced agentic AI systems.
The proposed policy-driven runtime layer shifts how multi-agent LLM systems will be deployed and managed, moving from ad-hoc solutions to integrated, policy-aware serving stacks.
- · AI infrastructure providers
- · Cloud AI platforms
- · Enterprises deploying agentic LLMs
- · Legacy LLM serving architectures
- · Organizations relying on simple, single-model serving
Improved efficiency, scalability, and safety for multi-agent LLM deployments.
Accelerated development and adoption of sophisticated agentic AI applications across industries.
Increased demand for specialized AI/ML engineers skilled in agentic system architecture and runtime management.
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Read at arXiv cs.AI