Q-Bridge: Agent-Directed KV Cache Lifecycle Management for Efficient LLM Serving

Agentic workloads repeatedly revisit, branch from, and abandon contexts across model and tool calls. However, execution intent available to the agent is not necessarily reflected in the request history used by inference systems to manage key–value (KV) caches. This mismatch can leave obsolete contexts resident while evicting contexts needed by subsequent calls. We propose Q-Bridge, an agent-directed protocol for KV cache lifecycle management. Q-Bridge lets agents issue explicit retention, release, and revision commands for identified contexts, while an infrastructure controller admits and executes these commands under memory and transfer constraints. Versioned lifecycle state persists across requests, allowing execution events to update cache decisions without requiring repeated state exchange before every model invocation. When intent is uncertain, the controller selectively requests additional information based on its expected decision benefit and communication cost. We formulate the resulting coordination problem as minimizing serving latency over an agent execution episode subject to cache capacity and communication constraints. Our evaluation targets persistent-cache workloads with interleaved agent executions, measuring time to first token from request arrival, including protocol overhead, alongside cache reuse, recomputation, and task quality. Q-Bridge provides a framework for studying explicit agent control over cache lifetimes and its tradeoffs against inference-only policies and unconditional state sharing.