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09:56

5 Agent Design Patterns for Long-running AI Agents

构建生产级长时间运行AI Agent的5种设计模式

Google Cloud presents five design patterns for building AI agents that run up to seven days: checkpoint-and-resume for state durability, delegated human-in-the-loop with zero-cost pausing, layered memory governance (Memory Bank, Profiles, Agent Identity/Registry/Gateway) against drift and leakage, ambient event-driven processing with externalized policies, and fleet orchestration using independently deployed specialists. Each pattern includes ADK code examples and diagrams, addressing production concerns like memory drift. Aimed at developers scaling agents from chatbots to autonomous workers.

x.com · 11 min · Agents · AI
09:56

Meta-Meta-Prompting: The Secret to Making AI Agents Work

Meta-Meta-Prompting：Garry Tan 如何用 AI 构建真正运转的第二大脑

Garry Tan, CEO of Y Combinator, presents GBrain, his personal AI agent system built on 100,000 pages of structured knowledge and over 100 modular skills. The core architecture follows a “thin harness, fat skills, fat data” philosophy: a lightweight runtime like OpenClaw routes messages to self-contained skill files, which are themselves created and improved by a meta-skill called Skillify. Tan illustrates the compounding value through the “book-mirror” pipeline, which cross-references a book’s ideas with his actual life events, journal entries, and meeting notes. He details the evolution from an error-prone first version to a reliable workflow using multi-model cross-modal evaluation and deep brain retrieval. Other examples include automated meeting preparation that synthesizes months of accumulated context and entity propagation that updates every related person or company page after a conversation. The article provides a concrete architecture overview, evidence of iterative improvement, and a four-step starting guide for developers building personal compounding AI systems.

x.com · 16 min · Agents · Ai Tooling · Knowledge Graph · Open Source
06:00

Designing for Agents: Patterns, Feedback, and Context

为 Agent 而设计：交互模式翻转与三条实践原则

Ramp’s MCP weekly active users grew 10x in 3 months; Salesforce launched Headless 360, signaling that 80% of software interaction is shifting to agents. The article proposes a new pattern: User → User’s Agent → Software’s Agent → Database, and offers three practical heuristics: proactively teach calling agents how to succeed (like Notion pre-loading a Markdown spec); build feedback loops via required rationale, a feedback tool, and purpose-built seeds; mind the context gap in agent-to-agent interactions by letting each side contribute what it knows best. Essential reading for product teams building agent-native interfaces.

x.com · 10 min · Agents · LLM
06:00

Getting the most out of Codex

不止写代码：Codex 持久线程、目标验证与自动化全景

This guide shows how to extend Codex from a code assistant to a persistent work system built around durable threads. Readers will learn: using pinned threads with shortcuts (Command-1–9) to preserve context across sessions; voice input for rough ideas; steering and queuing to correct or schedule tasks mid-flight; heartbeat-triggered thread automations (e.g., periodic Slack/Gmail checks); and long-running Goals with test verifiers. The side panel supports inline review of artifacts, while an Obsidian vault serves as shared memory for cross-thread decisions. For engineers integrating AI deeply into their daily workflow.

x.com · 12 min · Agents · AI
06:00

The Anatomy of an Agent Harness

Agent Harness 解剖：构建生产级 Agent 的 12 个组件

A deep dive into the 12 components of a production-grade agent harness, synthesizing practices from Anthropic, OpenAI, LangChain, and others. It argues that the harness—not the model—determines real-world agent performance, citing evidence like LangChain's 20+ rank jump on TerminalBench and Claude Code's 95% context reduction. Essential reading for engineers building or debugging AI agents.

x.com · 19 min · Agents · AI · LLM