标签 · AI Agents — Glean

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06-12

How To Build AI Agents in 2026 (That Actually Work)

This article systematically deconstructs the architecture and engineering practices for building practical AI agents. It clarifies the boundaries between chatbots, AI agents, and agentic AI, emphasizing that a real agent is a system that persistently loops toward a goal rather than delivering a one-shot answer. The author explains the ReAct loop (Reasoning + Acting) and breaks down the five building blocks: the LLM as the brain, tools as hands, short-term and long-term memory, self-correcting loops, and verification. Using a case study of a startup research agent for the fitness niche, the article walks through goal setting, tool integration, loop construction, memory implementation, and the addition of a critic agent, complete with copy-paste system prompts. It highlights six common failure modes and recommends a 2026 tech stack including Claude Code, LangGraph, and MCP. The piece provides a weekend roadmap to build a basic agent from a 50-line Python script and is aimed at developers shifting from prompt engineering to designing agent systems.

x.com · 21 min · Agent Architecture · AI Agents · AI Engineering

06-11

The Missing Link Between Agents and Applications

This article introduces Headless Tools, a mechanism that allows agents to act directly on client-side runtimes such as browsers and desktop applications. The author argues that most current agent tools are server-side, limiting them to API calls while blocking access to browser state, device APIs, and in-app actions. Headless Tools wrap client-side capabilities like geolocation, clipboard, IndexedDB, and application-specific commands as standard tools invocable by the model. The model sees only a tool schema, while the server and client coordinate execution behind the scenes. Code examples in TypeScript demonstrate the pattern, alongside real-world use in a Slidev presentation plugin and browser-local agent memory. Privacy is improved because sensitive data can remain on-device. This is valuable for teams embedding AI agents into rich frontend contexts such as design tools, document editors, and desktop utilities.

x.com · 7 min · AI Agents · AI Engineering · Browser

06-10

AI Agent Skill: Cross-Platform Social Search and 30-Day Synthesis

/last30days is an AI agent skill that aggregates the latest content from Reddit, X, YouTube, TikTok, Hacker News, and more into a 30-day briefing. It uses entity pre-research to identify key people, communities, and topics, then searches in parallel and scores by real engagement (upvotes, likes, money) rather than SEO. An AI synthesizes a cited, in-depth summary. Open-source (MIT), it supports Claude Code and 50+ agent frameworks. Ideal for engineers, PMs, and researchers needing a quick, grounded update before meetings or decisions.

github.com · 27 min · AI Agents · Open Source · Social Media

06-10

Designing loops with Fable 5: self-correction and memory in agentic workflows

The author shares two practical directions for improving agentic workflows with Anthropic's Claude Fable 5 model: self-correction loops and cross-session memory. In a Parameter Golf challenge—train the best model within a 16MB artifact in under 10 minutes on 8×H100 GPUs—Fable 5 improved the training pipeline roughly 6× more than Opus 4.7 when using Claude Managed Agents with Outcomes judged by an independent verifier sub-agent against nine checkable criteria. Fable 5 bet on larger structural changes and pushed through a quantization regression, while Opus 4.7 stuck to tuning scalar hyperparameters. For memory, the author used a SQL-based task from Continual Learning Bench 1.0 with filesystem-backed memory across agent sessions. Sonnet 4.6 only logged failures and guesses; Opus 4.7 built flagged schema references but verified only 17% of questions; Fable 5 reached 73% verification coverage in the best run and distilled learnings into general rules. Engineers interested in agent architecture and model capability boundaries will find the experiments relevant.

x.com · 5 min · Agent Architecture · AI Agents · AI Engineering

06-09

Loop Engineering: Designing the System That Prompts Your Coding Agents

Addy Osmani argues that interacting with coding agents is shifting from prompt engineering to 'loop engineering'—designing a system that autonomously discovers tasks, delegates work, and verifies results using five building blocks: scheduled automations, parallel worktrees, project skills, connector plugins, and checker sub-agents. He maps how Claude Code and Codex both implement all five, noting that the leverage point has moved from writing good prompts to architecting persistent loops. The post cautions that loops amplify existing problems: verification, comprehension debt, and cognitive surrender become sharper risks. Intended for senior engineers evaluating how to productize AI coding tools beyond one-shot interactions.

x.com · 14 min · Agent Architecture · AI Agents · AI Engineering