In production Go agents, the first thing that breaks is usually not model quality. It is memory management: context grows, bills spike, and answers drift. Use a 3-layer memory design: L1: short-term conversational window (seconds) L2: rolling summary (minutes) L3: long-term retrieval memory (days) ...
你在 Go 里做 Agent，最容易翻车的不是推理能力，而是“记忆”失控：上下文越来越长、账单越来越高、回答却越来越飘。 这篇给你一个可落地的三层方案： L1：短期会话上下文（秒级，强相关） L2：中期摘要记忆（分钟级，压缩） L3：长期检索记忆（天级，向量索引） ...
If you’ve already used function calling but keep writing glue code for every non-trivial task, you’re likely at the point where Responses API + MCP makes more sense. This guide is practical: how to move from single tool calls to a scalable agent workflow where retrieval, execution, validation, and write-back follow a consistent structure. ...
如果你已经做过函数调用（function calling），但一上复杂流程就开始写一堆胶水代码，那你差不多到了该用 Responses API + MCP 的阶段。 这篇不讲空概念，直接给你一个可落地的路线：把“模型调用工具”升级成“可扩展 Agent 工作流”，让检索、执行、校验、回写变成标准流程。 ...