This article details the design, operation, and continuous improvement of a six‑agent autonomous system, covering memory management, protocol design, self‑healing mechanisms, and practical engineering lessons for building reliable, self‑evolving AI workflows.
This article explains how AI engineering has evolved from Prompt Engineering to Context Engineering and now Harness Engineering, detailing each stage's challenges, core techniques, and real‑world practices that turn large language models into reliable, long‑running production systems.
The article presents an AI‑driven microservice governance platform that employs a multi‑agent architecture—Director, Coder, Ops, and Diagnosis agents guided by SOP‑encoded prompts—to enable natural‑language, full‑process interactions, LLM‑based root‑cause analysis, and a continuous data‑flywheel that evolves models through large‑scale dialogue evaluation.
The article introduces Jarvis, an AI-driven microservice governance platform that uses a multi-agent architecture and natural-language dialogue to automate full-process operations such as deployments, rate limiting, and circuit-breaker configuration, while leveraging large language model reasoning for root-cause diagnosis and a data-flywheel that continuously trains lightweight expert models.
