This article dissects DeepSeek Harness's unified Session Log architecture where context management, memory compaction, and knowledge acquisition collaborate through layered context assembly, structure-preserving compaction, and tool-mediated retrieval — all traceable and replayable.
The article analyzes the engineering challenges of large‑scale multi‑agent AI systems—such as connection loss, synchronous blocking, and traffic spikes—and explains how Huawei Cloud AI Queue, built on RocketMQ with Lite Topic, Session Log, and priority topics, provides an event‑driven, elastic, and observable communication backbone for stable, scalable agent collaboration.
The article breaks down five concrete design patterns in DeepSeek Harness—session logs as truth, repeat‑tool reminders, output retention, Code Mode governance, and Workspace for long‑term state—showing how the framework shifts agent development from pure model tricks to a full‑featured runtime system.
The article analyzes the structural flaw of tightly coupling session, harness, and sandbox in a single container, proposes separating them into three independent interfaces, and demonstrates how this redesign improves fault tolerance, security, and reduces Time‑to‑First‑Token latency by about 60% while enabling flexible, scalable agent deployments.
