标签 · AI Agents — Glean

Recent picks

18picks · chronological

09-08

AI-native no-code platform for building business systems with agents

NocoBase is an open-source, self-hostable platform that blends AI with no-code development for building business systems such as CRM, ERP, project management, and internal tools. Instead of treating AI as a black box that generates fragile code from scratch, it lets coding agents (Claude Code, Cursor, Codex, OpenCode, etc.) work on tested infrastructure while human users build and refine the same system through a WYSIWYG visual editor. The platform includes configurable AI employees that can join workflows—handling document recognition, risk monitoring, Q&A, and form filling—with role-based and field-level permissions plus audit logging. External agents can connect via MCP, HTTP APIs, CLI, or messaging channels such as Telegram, WhatsApp, Slack, and Gmail. With a microkernel and plugin architecture, business data stays in your own relational database, decoupled from UI and extensions. This makes NocoBase a pragmatic reference for engineers and teams exploring low-code systems, AI agents, and human-AI collaboration.

github.com · 9 min · AI Agents · Low-Code · No-Code

08-20

A shared browser for AI agents: parallel automation, shared logins

ego lite is a macOS browser purpose-built for AI agents such as Claude Code and Codex. It lets agents run browser automation in parallel inside isolated Spaces while you continue browsing in the same browser — no tab wars, no interrupted flow. Unlike frameworks like Browser-use that drive a separate browser, ego lite is a full browser that can inherit your existing Chrome logins, cookies, extensions, and bookmarks in one click. It exposes browser capabilities as in-page JavaScript tools via the `ego-browser` skill, so agents can compose multi-step actions as single code executions instead of round-trip CLI calls. The README reports up to 2.5× faster task completion and fewer tokens than a CLI-based approach on complex tasks, with data kept locally by default. For engineers using agent CLIs today, ego lite offers a straightforward way to give agents safe, stateful access to the web while keeping your own session separate.

github.com · 10 min · Agent Skills · AI Agents · Browser Automation

08-10

Cloudflare OS: an open source agent workspace with capability-based governance

Cloudflare has open-sourced Cloudflare OS, the agent workspace it uses internally, to bring AI leverage beyond programmers. A workspace is grounded in company-curated context and skills, and starts each agent with zero access. Resources are exposed as typed bindings, while Gatekeepers—service-specific Workers—hold credentials, enforce policy, and log every observed resource. Sharing a dashboard does not become a way to leak an underlying table, because the platform re-checks the viewer against the observation log. Apps are full-stack Workers with their own SQLite state, and the same server methods are callable by both users and agents. Model routing and spend run through AI Gateway, so admins can set budgets and pick models per task. The code is on GitHub and deployable into a Cloudflare account, with a reference deployment included. Written for engineers working on agent infrastructure, access control, and internal development platforms.

blog.cloudflare.com · 16 min · Agent Infrastructure · AI Agents · Capability Security

08-10

Cloudflare OS: an AI workspace with per-user sandboxed apps

Cloudflare OS is an open-source AI productivity environment that Cloudflare built for its own workforce. It acts as an operating system for both company operations and AI workloads: instead of fixed document types, every file is a 'Gadget'—a private, sandboxed application that an agent can create, modify, and debug on the user's behalf. A capability-based security layer called Gatekeepers brokers access to external services, logging side effects and letting users approve queued actions in bulk later instead of blocking the agent mid-task. The entire platform runs on Cloudflare Workers and workerd, leveraging Durable Objects, Dynamic Workers, and Facets, and every Gadget has real-time multiplayer collaboration by default. Blueprints let users share the code of an app so others can spawn their own copies. It is early access but substantial, and a valuable reference for engineers exploring agent infrastructure, per-user app sandboxing, and edge-based AI tooling.

github.com · 23 min · Agent Architecture · AI Agents · Cloudflare

08-08

Cloudflare ADLC: Workflow-based CI/CD for agent software factories

Cloudflare argues the bottleneck in software development has moved from implementation to every other SDLC stage, now that AI makes code generation cheap. Their answer: let agents drive more of the lifecycle, not just codegen. The post introduces @cloudflare/ci, local OTel traces for Wrangler, Agent Traces, and a set of primitives meant to turn the SDLC into an 'Agent Development Lifecycle' for software factories. It includes Workflow code that parallelizes lint/test/typecheck/build and then deploys, plus guidance that CI/CD is just one kind of Workflow—workflows can spawn containers, agents, and browsers and persist state for days. The article also lists seven platform requirements for agent-driven delivery: programmatic, horizontally scalable, reproducible, push-based, atomic, permissioned, and self-improving. Useful for engineers building agent infrastructure on Cloudflare or exploring autonomous delivery pipelines.

blog.cloudflare.com · 14 min · Agent Engineering · AI Agents · Cloudflare

08-07

Introducing Cloudflare Agents: Agent Tracing and Observability

Cloudflare introduces Cloudflare Agents, a unified console to deploy and manage hosted agents, launching with agent tracing. The feature adds agent-aware spans for model calls, tool executions, approval events, token usage, and supported subagent calls, overlaying existing Workers infrastructure traces (fetch, KV, D1). It is initially compatible with Think, Flue, and AI SDK, with plans to accept OpenTelemetry semantic conventions directly in Workers. Developers can replay recorded sessions to inspect full conversation context, or view execution waterfalls that show subagent delegation and the Cloudflare resources used. Traces can be exported to any OTLP destination, enabling evaluation and analytics. Pricing is based on existing Workers observability events, free during beta and then included in Workers Free/Paid plans. The feature targets teams running agents on Cloudflare who need behavior-level insight beyond traditional telemetry.

blog.cloudflare.com · 9 min · Agent Engineering · AI Agents · Cloudflare

08-06

Jeff Dean's Last Interview: AI Speed, the 0% Rule, and His Google Exit

In his last interview before leaving Google, Jeff Dean admits his year-old prediction undershot reality: models are improving faster than expected at complex tasks, and agentic systems can now run continuously for weeks. He likens specialized inference hardware to the 2001 'put the search index in RAM' moment, citing 50x latency and 30-80x energy-efficiency gains. A single compute op costs ~1 picojoule; moving data costs 1000x more, which forces batching. The conversation covers context engineering as the new frontier, the napkin math behind TPU, and a founder heuristic: test your domain with the strongest general model — 0% or 1% success is a good sign, 20% is a red flag. Once agents write all the code, he argues, taste — knowing what to have agents solve — becomes the scarce skill. Relevant for AI infrastructure engineers, agent builders, and AI founders.

www.infoq.cn · 14 min · AI Agents · Context Engineering · Inference Hardware

07-23

Turn any codebase into a queryable knowledge graph, built for AI coding assistants

Graphify is an open-source tool that transforms codebases, docs, PDFs, images, etc. into a queryable knowledge graph. It uses tree-sitter for local deterministic AST parsing to extract code relationships (calls, imports, inheritance) without any LLM calls. Non-code files are semantically extracted via your AI assistant's model. The output includes an interactive HTML visualization, CLI queries (query/path/explain), and an MCP server for team use. Every edge is tagged EXTRACTED or INFERRED, providing transparency. Ideal for engineers navigating large monorepos, tracing dependencies, or understanding architecture.

github.com · 59 min · AI Agents · Developer Tools · Knowledge Graph

07-22

Kimi Code CLI: Terminal AI Coding Agent for Next-Gen Agents

Kimi Code CLI is an AI coding agent that runs in your terminal. It reads and edits code, runs shell commands, searches files, fetches web pages, and autonomously decides next steps based on feedback. It ships as a single binary with millisecond startup, features a purpose-built TUI, supports video input, AI-native MCP configuration, a rich plugin ecosystem, subagents for parallel tasks, lifecycle hooks, and ACP integration with editors like Zed and JetBrains. Ideal for developers seeking a powerful, extensible AI coding companion.

github.com · 4 min · AI Agents · CLI · Developer Tools

07-16

The Short Leash AI Coding Method For Beating Fable

This post distills over a year of research on using AI agents for security-critical software. The author introduces the “Short Leash” method: only expert developers can use it; never enable YOLO mode; manually review every diff in the permissions prompt to keep the AI on track; commit after each subtask to safeguard against regressions. It also details AI-assisted code review: pair human and AI, with AI catching surface errors and humans guiding direction. PR authors must self-review line-by-line and disclose AI models used. This approach beats Fable even with non‑frontier models, without sacrificing quality. Targeted at senior engineers who want productivity gains without giving up understanding.

blog.okturtles.org · 7 min · AI Agents · AI Engineering · Code

07-03

Self-Healing Browser Harness That Lets LLMs Drive Any Real Browser

Browser Harness is a thin, self-healing CDP harness that connects an LLM directly to a real Chrome browser via a single WebSocket, with zero intermediate layers. When the agent needs to perform an action it hasn't seen before (e.g., file upload, cross-origin iframe interaction, drag and drop), it writes the missing helper code on the fly and saves it into an agent-workspace for reuse. The core package is roughly 1K lines, enabling complete freedom for browser automation tasks. Aimed at developers who need AI agents to perform real, unconstrained browser interactions.

github.com · 7 min · Agent Engineering · AI Agents · Browser Automation

06-30

Browser Automation CLI for AI Agents

agent-browser is a native Rust CLI designed for AI agents to automate browser interactions. It uses a client-daemon architecture where the Rust daemon directly communicates with Chrome via CDP, eliminating the Node.js dependency. The tool offers a comprehensive command set covering navigation, element interaction (via ref/CSS/XPath/text selectors), snapshots, screenshots, network interception, session management, and authentication state persistence. It includes built-in safety features like domain allowlists, action policies, and encrypted state storage. It is optimized for AI workflows with accessibility tree snapshots, annotated screenshots, and MCP server support, making it ideal for engineers building AI agents, automated testing, web scraping, or enabling LLMs to control browsers reliably.

github.com · 64 min · AI Agents · Browser Automation · CDP

06-18

A Structured Cybersecurity Skills Library Purpose-Built for AI Agents

This is not another collection of security scripts or checklists. It’s an AI-native knowledge base that encodes 754 practitioner-grade cybersecurity workflows into a structured, agent-readable format. Each skill carries YAML frontmatter for sub-second discovery and step-by-step Markdown procedures, essentially giving any LLM-based agent the decision-making playbook of a senior analyst. The library spans 26 domains—from DFIR and threat hunting to cloud security and OT/ICS—and maps every skill to MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, and NIST AI RMF, making it uniquely suited for security professionals integrating AI into real operational workflows.

github.com · 28 min · AI Agents · Claude Code · Cybersecurity

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