PBKV: A Prediction-Based KV Cache Management System for Dynamic Agent Workflows

This article introduces PBKV (Prediction-Based KV Cache Management System for Dynamic Agent Workflows), whose core is to optimize KV cache management by predicting future agent call sequences. It solves the problem that traditional methods cannot effectively utilize cache reuse opportunities in dynamic workflows and achieves a maximum speedup of 1.85x in dynamic scenarios.

LLM-based agent workflows decompose tasks into multiple steps handled by specialized agents, improving task quality but introducing cache challenges: different agents share a large amount of context, and KV cache reuse can reduce redundant computations. However, existing methods have limitations—single-agent-level management fails to leverage workflow-level reuse, or assuming fixed agent sequences cannot handle dynamic workflows (where agent call order is determined by task context).

The core idea of PBKV is to predict future agent calls to plan cache strategies:

1. Prediction Mechanism: Integrates historical workflow execution patterns and current context features to perform rolling prediction (updating future steps' predictions after each execution step), outputting a probability distribution of agent calls;
2. Cache Decision-Making:
   • Eviction: A conservative strategy that only evicts cache entries predicted to be "very unlikely to be needed";
   • Prefetching: Conservatively prefetches entries predicted to be "very likely to be needed" into GPU memory.

To address imperfect predictions:

• Conservative eviction/prefetching strategies reduce the impact of prediction errors;
• A feedback loop monitors prediction accuracy and adjusts model parameters or strategies;
• Fast recovery mechanism: Load evicted but actually needed entries from CPU memory/disk, which is faster than recomputing.

In benchmark tests of dynamic workflows such as multi-turn dialogues, tool call chains, and conditional branches:

• Achieves a maximum speedup of 1.85x compared to the LRU strategy;
• Achieves a 1.26x speedup compared to KVFlow in static workflows;
• Ablation experiments verify: Fusion prediction (history + context) outperforms single-source prediction, and conservative strategies have better average performance.

Deployment Considerations: The prediction module is lightweight (with millions of parameters), cache metadata has linear memory usage, and cross-GPU coordination is supported; Application Scenarios: Multi-agent collaboration systems, conditional branch workflows, context-sharing task pipelines; Future Extensions: More complex prediction models (e.g., Transformer sequence prediction), reinforcement learning for optimizing cache decisions, support for multimodal workflows.

PBKV makes informed cache decisions in dynamic systems by predicting future agent call sequences, significantly accelerating execution. The 1.85x speedup can improve hardware resource utilization or reduce costs, providing a validated cache management solution for agent system developers, which is worth considering for practical deployment.