Get Experience from Practice: LLM Agents with Record&Replay

2025-05-23

AI Summary

To address systemic challenges in LLM-based agents—namely reliability, privacy, cost efficiency, and performance—this paper proposes AgentRR, a novel paradigm that introduces a “record-and-replay” mechanism into LLM agent frameworks. It systematically records task execution traces and abstracts them into structured, reusable “experiences.” The method features multi-level experience abstraction, safety- and generalization-aware check functions, and an experience repository supporting user demonstration recording, collaborative inference between large and small models, and privacy-sensitive execution. Experiments demonstrate that AgentRR significantly improves execution consistency and reliability, reduces redundant computation and API call costs, ensures compliance with privacy regulations, and enables cross-task and cross-user experience reuse. By promoting efficient knowledge retention and transfer, AgentRR advances the deployment of lightweight, trustworthy LLM agents.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Agent Communication
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

AI agents, empowered by Large Language Models (LLMs) and communication protocols such as MCP and A2A, have rapidly evolved from simple chatbots to autonomous entities capable of executing complex, multi-step tasks, demonstrating great potential. However, the LLMs' inherent uncertainty and heavy computational resource requirements pose four significant challenges to the development of safe and efficient agents: reliability, privacy, cost and performance. Existing approaches, like model alignment, workflow constraints and on-device model deployment, can partially alleviate some issues but often with limitations, failing to fundamentally resolve these challenges. This paper proposes a new paradigm called AgentRR (Agent Record&Replay), which introduces the classical record-and-replay mechanism into AI agent frameworks. The core idea is to: 1. Record an agent's interaction trace with its environment and internal decision process during task execution, 2. Summarize this trace into a structured"experience"encapsulating the workflow and constraints, and 3. Replay these experiences in subsequent similar tasks to guide the agent's behavior. We detail a multi-level experience abstraction method and a check function mechanism in AgentRR: the former balances experience specificity and generality, while the latter serves as a trust anchor to ensure completeness and safety during replay. In addition, we explore multiple application modes of AgentRR, including user-recorded task demonstration, large-small model collaboration and privacy-aware agent execution, and envision an experience repository for sharing and reusing knowledge to further reduce deployment cost.

Problem

• Address LLM uncertainty and resource challenges in AI agents
• Improve agent reliability, privacy, cost, and performance
• Propose record-and-replay mechanism for efficient task execution

Innovation

• AgentRR introduces record-and-replay mechanism
• Multi-level experience abstraction balances specificity
• Check function ensures replay safety