@grapeot: Very well said, hits the nail on the head.
Summary
A discussion about using AI Agent for system design and coding in a microservices environment, highlighting the need for the AI to understand service boundaries and business concepts.
View Cached Full Text
Cached at: 07/02/26, 08:20 AM
Well said, hitting the nail on the head.
宝玉 (@dotey): Q: Our company has a dozen microservices, and now we want our developers to use AI Agent for system design and coding. The problem is that a user story often requires collaboration across multiple microservices, so the Agent must understand the responsibility boundaries and business concepts of each service to make reasonable designs. We plan to put all microservices into one workspace, with each service having its own documentation, so that AI…
Similar Articles
@dotey: Q: Our company has a dozen microservices, and we want developers to use AI Agents for system design and coding. The problem is that a user story often requires collaboration among multiple microservices, and the Agent must understand each service's responsibility boundaries and business concepts to make reasonable designs. We plan to put all microservices into a single …
The article discusses in a Q&A format how to enable AI Agents to perform system design and coding in a multi-microservice scenario, focusing on practical experiences with context quality (via monorepo, layered documentation) and validation loops (via contract testing, mock servers).
@knoYee_: https://x.com/knoYee_/status/2062780637677752366
The author reviews three months of experience using multi-agent collaboration, summarizing five main pain points (such as conflicts between agents, ignoring boundary conditions, self-censorship failure, difficulty in merging decisions, and exposing harder problems after compressed execution) and two insights (the high value of read-only review agents, and that agent conflicts expose ambiguous requirements), emphasizing the core decision-making role of humans in AI collaboration.
This article systematically reviews AI Agent architecture and engineering practices, covering control flow, context engineering, tool design, memory, multi-agent organization, evaluation, tracing, and security. It is based on the OpenClaw implementation and emphasizes the critical role of Harness (testing and validation infrastructure) for system stability.
This article systematically reviews AI Agent architecture and engineering practices, covering control flow, context engineering, tool design, memory, multi-agent organization, evaluation, tracing, and security. It is based on the OpenClaw implementation and emphasizes the critical role of Harness (testing and validation infrastructure) for system stability.
@yanhua1010: Are you also confused by the many concepts of AI Agent (Harness, Scaffold, Context Engineering...)? Recently, I saw an article from Huggingface in the @teach_fireworks teacher community explaining Agents...
Recommending a Huggingface article that explains common terms of AI Agent to help clarify concepts like Harness, Scaffold, Context Engineering, etc.
@lidangzzz: I've said it many times over the years: to make an AI Agent write good code, all the secrets are in the textbooks from the 1990s: - Write tests diligently, write more tests, push test coverage as high as possible - Do CI/CD properly, avoid messing up at all costs - For a new proj…
The author emphasizes that the key to making AI agents write good code lies in following classic software engineering practices from 1990s textbooks: writing tests, doing CI/CD properly, top-down design, and modular decoupling.