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06:01

Claude Official Cookbooks: Engineering Recipes from RAG to Multimodal Agents

Claude 官方实践手册：从 RAG 到多模态 Agent 的工程配方集

Anthropic's official collection of practical coding recipes for building with Claude. It provides runnable Jupyter notebooks covering capabilities like classification, summarization, and RAG, alongside advanced techniques such as tool use, multimodal vision, and sub-agent orchestration. The latest additions introduce the Claude Agent SDK and Managed Agents, demonstrating how to build observable, hostable agents—from research assistants to SRE bots—with just a few lines of code.

github.com · 8 min · Agents · AI Engineering · Anthropic · Claude Code · Jupyter-Notebook · Rag
06:01

Decomposing the agent harness into swappable workers: the iii engine architecture

将 agent harness 拆解为可独立替换的 workers：iii 引擎的架构实验

Mike Piccolo argues that monolithic agent frameworks force a tradeoff by bundling the loop, tools, memory, and orchestration into one block, which long-running teams inevitably rewrite. He walks through the iii engine's production worker stack, where all thirteen harness responsibilities—credential resolution, policy checks, turn FSM, session persistence, budget tracking, etc.—are decomposed into 11 independently replaceable workers. Each worker connects to the engine via WebSocket and registers functions and triggers using a single primitive (iii.trigger()), making the harness a composable set of installable workers. The post provides a step-by-step trace of a turn through provisioning, streaming, policy-gated tool dispatch, and reactive approval wake-ups, alongside concrete examples of swapping the model catalog, adding a provider, or integrating a Slack approval surface. The core bet: an agent harness should be a slider of composable workers rather than a framework you fork. This is for backend engineers building or scaling custom agent infrastructure who are hitting the composability limits of existing frameworks.

x.com · 20 min · Agent Architecture · AI Engineering · Observability · Open Source · Orchestration
06:01

A frontier without an ecosystem is not stable

前沿模型若无生态系统，便不稳定

Satya Nadella argues that the future of the firm in an AI-driven economy relies on creating a compounding learning loop that integrates human capital and AI 'token capital.' He emphasizes that organizations must build agentic systems that own their institutional knowledge and private RL environments, ensuring they can swap underlying models without losing proprietary expertise. Warning against a future where a few models commoditize all value, he advocates for building a 'frontier ecosystem' that enables broad value distribution across every industry, rather than solely chasing a frontier model. This piece targets executives and senior technologists strategizing AI adoption.

x.com · 5 min · AI Engineering · AI Industry · Context Engineering