标签 · Agent Infrastructure

Recent picks

5picks · chronological

07-18

What we’ve learned building cloud agents

Cursor shares one year of lessons from building cloud agents. The biggest takeaway: a cloud agent's output quality depends almost entirely on having a full development environment. Unlike local agents that inherit your laptop's environment, cloud agents need it reconstructed from scratch. This led to building VM hibernation/resumption pipelines, checkpoint/restore/fork mechanisms, secret redaction, network policies, and credential management — essentially enterprise IT for agents. For reliability, early work-stealing architecture managed only one 9 of uptime. Migrating to Temporal's durable execution framework pushed past two 9s, handling over 50 million actions per day across 7 million+ unique workflows, with 40%+ of Cursor's own PRs now coming from cloud agents. Critical architectural decision: decoupling agent loop, machine state, and conversation state allows agents to run across different pod types and subagents to outlive parents. Another insight: as models improve, move logic out of the hardcoded harness into tools the agent controls (e.g., GitHub CLI, Playwright). The post also discusses current harness for computer use and future self-healing environments (autoinstall).

cursor.com · 11 min · Agent Architecture · Agent Infrastructure · Agents

07-02

Building a Good Vertical Agent: Context as a Cache Hierarchy

The article argues that a good vertical agent is a faithful compression of its task distribution, and its context should be organized as L1/L2/L3 cache tiers. Using their Shortcut spreadsheet agent as example, they detail extreme optimizations: reading a range compresses 500 formulas into a single legend line via R1C1 normalization and aliasing; after writing, a structured diff groups, samples, and triages changes, flagging #REF! errors under MUST FIX. L2 provides curated English specs fetched on demand, like the pivot table recipe that bakes in gotchas (suspendLayout/resumeLayout, raw integer 8 for aggregation). L3 is the raw API reference plus a 100-line grep skill that lets the model mine tens of thousands of lines in bounded steps. The prompt budget mirrors the frequency curve, and the hierarchy moves as models improve. Practical, transferable advice for engineers building reliable agents in any domain.

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

06-29

Temporary Cloudflare Accounts for AI Agents

Cloudflare introduces temporary accounts for AI agents, enabling deployment via `wrangler deploy --temporary` without manual signup. The accounts last 60 minutes, during which agents can iteratively deploy and developers can permanently claim them. The post addresses the problem of background AI sessions getting stuck at browser-based OAuth flows and explains how the CLI prompts agents about the flag for discovery. A complete TypeScript demo walks through deploying a hello world Worker, modifying it, and redeploying with verification. Partnerships with Stripe and WorkOS are noted as part of broader efforts to reduce agentic deployment friction. Target readers include agent platform builders and developers using coding agents.

x.com · 1 min · Agent Infrastructure · Agents · CLI

06-17

How to build a self-improvement loop for your Skills

This article demonstrates a practical approach to building a self-improvement loop for AI Skills using inner and outer agent loops. The inner loop triggers a cloud agent via GitHub Action on each new issue, applying a triage Skill to classify it. The outer loop runs daily, reviews all human corrections (label changes and comments), and generates a diff to update the Skill file, which is then merged back. The author uses Warp's Oz cloud agent platform for issue triage, providing complete code and a sample repo. The pattern is generalizable to code review, bug fixing, and incident response. Suitable for engineers building AI agents who want to improve skill quality over time.

x.com · 4 min · Agent Engineering · Agent Infrastructure · Orchestration

06-08

Every Agentic Engineering Hack I Know (June 2026)

The author shares 22 practical hacks for agentic engineering with Claude Code and Codex. The core is a plan-first workflow: use /ce-plan to generate a plan.md that guides the agent; humans skim or ask inline instead of reading it. Hacks include: voice input via Monologue or Wispr Flow (LLMs handle imperfect transcription); running 4-6 separate agent sessions in cmux tabs; defaulting terminal tabs to Claude Code and bypassing all permission prompts with sound alerts on completion; giving Claude an email address via AgentMail to trigger sessions remotely; using last30days before planning to search community discussions and news in parallel; turning repeated tasks into reusable skills to compound agent capabilities. He stresses that human value lies in providing taste and direction, not typing, and warns against AI addiction. The post is packed with copy-paste config snippets and concrete tools, aimed at engineers deep into AI-assisted development.

x.com · 28 min · Agent Infrastructure · Agents · AI Engineering