告别Demo、真正跑进生产，华为新框架把Agent端到端效率拉升2.5倍

Core Insights - The article discusses the transition of large model agents from demonstration to production, highlighting challenges in real-world workflows where advanced reasoning may fail during implementation [2] - It introduces AgentInfer, an end-to-end acceleration framework designed for industrial agents, which optimizes both inference architecture and service systems collaboratively [2] Group 1: Challenges in Agent Performance - Traditional metrics like tokens/s and single latency are insufficient for evaluating agent performance, as agents operate in a continuous Think-Act-Observe cycle [4] - Quantization can lead to faster single-step inference but may degrade overall task success rates, resulting in increased retries and longer total time-to-solution [5][6] - Summarization techniques can reduce token usage per step but may increase the number of turns required to solve tasks, leading to cognitive ambiguity [7] Group 2: Memory and Context Management - High concurrency can lead to frequent eviction of long-context KV-caches, causing delays and reduced system throughput [8] - The efficiency of agents is not merely about speeding up each step but rather about minimizing ineffective turns, reducing recomputation, and enhancing cross-turn reuse [8] Group 3: Components of AgentInfer - AgentInfer consists of four independent and complementary modules that address different layers of performance issues [10] - AgentCollab focuses on collaborative efforts between small and large models, optimizing resource use while maintaining quality [12][13] - AgentCompress employs semantic compression and asynchronous distillation to manage context effectively without losing reasoning memory [14][16] - AgentSched introduces a control signal to adaptively switch between prioritizing short requests and maintaining KV-cache persistence [20] - AgentSAM leverages historical session data to enhance decoding efficiency and reduce redundancy in agent responses [21] Group 4: Performance Improvements - The integration of the four modules in AgentInfer has shown significant improvements in overall query per second (QPS) under high concurrency, achieving up to 2.52x improvement [24] - The modular design allows for incremental implementation, where each component can provide benefits independently while also enhancing overall system performance when combined [26] - AgentInfer aims to reduce ineffective token consumption by over 50% and achieve 1.8x to 2.5x end-to-end acceleration while maintaining task accuracy [29]