The guide breaks down AI agent engineering into four critical challenges—model selection with tiered routing, precise system‑prompt engineering, robust error handling with retry and budget guards, and token‑aware cost control—showing how each can cut costs 60% to 15× and push success rates above 95%.
Analyzing 17 weeks of GitHub trending projects from March to July, the author shows how the focus of AI agents has moved from role‑based demos to production‑grade concerns such as worktree isolation, model routing, cost, security, and multi‑agent orchestration, outlining four evolutionary stages and five key evaluation criteria.
The article explains how unlimited context causes long‑running AI agents to solidify early noise into false facts, and presents a three‑step protocol—memory half‑life configuration, conflict‑isolation sandbox prompts, and system‑level command isolation—that dramatically reduces response time and token waste.
The article explains that as AI agents grow more capable, merely tweaking prompts or adding context fails to ensure stable long‑term performance; instead, a systematic Harness Engineering layer that enforces constraints, validates actions, and automates feedback is essential for reliable agent operation.
The article defines Harness Engineering as the system that couples AI models with constraints, feedback loops, and documentation, explains why the agent alone is insufficient, details five concrete harness artifacts and five universal principles derived from OpenAI, Anthropic and ThoughtWorks case studies, and reveals the paradox that harnesses must be built to be removed as models improve.
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.
