What is the problem you're trying to solve

There has been increasing discussions and interests about volcano's plan to support AI Agent Workloads recently, this issue is to initiate the discussion and track all the community efferots regarding this direction.

Describe the solution you'd like

Abstract

TL;DR

• Problem: Modern AI is shifting from stateless, single-request inference to long-lived, stateful, session-based Agent workloads that current Kubernetes scheduling patterns do not handle well (startup latency, state persistence, fine-grained scaling, eviction sensitivity).
• Proposal: Create a Volcano sub-project to add native support for AI Agent workloads (workload CRD, lifecycle manager, agent-aware scheduling policies, and fast startup/hibernate mechanisms).
• Why it matters: Proper support improves resource utilization, reduces cold-start/state-loss risk, and enables multi-tenant, production-grade Agent deployments on Kubernetes.
• Scope: Design-focused initial effort—API/workload model, scheduling primitives, lifecycle and warm-pool mechanics; integration and reuse of existing OSS where possible.
• Call to action: Request feedback, real-world requirements, and collaborators to refine design and drive an agent-box prototype under volcano-sh.

Proposal Details

The full proposal working doc is at: Google Doc: New Subproject to Provide Native Support for AI Agent Workloads in Volcano

Will send PR to the repo when we have a more concrete version.

Additional context

Please feel free the share your thoughts and ideas

Update Oct. 24th, 2025

The repo name agent-box might be a bit too generic and easily overlap with other projects.
I'd like to update that we use agentcube instead.