# agent

Claude Tag is Anthropic's second-generation Claude in Slack, launched June 2026. Unlike typical chatbots: one thread is one persistent work session, and anyone in the channel can jump in mid-conversation to steer it. But the sandbox evaporates, and credentials never enter the sandbox — this security design is the baseline we'll compare against when we build our own.

Claude tag only officially supports Slack. Want an AI assistant that comes when you call on LINE? Build it yourself. Use OpenClaw to spin up a LINE bot on a VPS — messages come in from LINE's official cloud, the gateway catches them, and the agent replies. This post covers the replicable skeleton, three security must-haves, and why freedom always comes with responsibility.

Jarred Sumner rewrote 535K lines of Zig into Rust with 64 Claude agents in parallel, adversarial code review, and mechanical porting. 11 days later: all tests green, memory leaks fixed, binary 20% smaller.

Phil Chen shares six years of career lessons — from his own startup through Helm AI, Scale AI, OpenAI, and Google: when agents can solve every well-defined problem, what stays valuable is finding problems, sprinting the last mile, and everything that cannot be graded by a loss function.

Open any agent skill and it's stuffed with 'be more detailed,' 'be thorough'—lines that look diligent but don't change the model's behavior at all. Matt Pocock names the no-op trap, plus how to spot a dead instruction versus one that actually pulls its weight.

Sakana ships Fugu: a multi-agent orchestration system behind one API, sold as "AI sovereignty." But a researcher who read the tech report tears it down — a closed orchestrator on closed models means you control less, not more, and it wins benchmarks while never reporting cost.

Most coding-agent best practices from six months ago are now out of date. The new playbook: bigger tasks, longer sessions, and adversarial review so the agent verifies its own work — the engineer just shovels coal into the engine.

Loop engineering is sold as designing orchestration and spinning up agents — but the tools now do that half for you. The half still hard, still deciding the result, is the verifier. Anthropic's Bun port is the tell: 99.8% of tests pass, yet the announcement says not yet in production.

Every two hours, Codex drafts email replies for review. The drafts are good — he edits them anyway. Those edits are context too, and most automations throw them away. The fix: an inner loop brings context to the work; an outer loop recovers context from the review diff.

A six-word phrase seized the AI-coding timeline, but nobody boosting it agreed what it meant. This is not the how-to; it is why the loop blew up, its five-year lineage, why the loop is now the costly part, and why the durable asset is the skill it calls.

Microsoft CEO Satya Nadella on the future of the firm in an AI economy: build two kinds of capital — human capital and token capital. The real moat isn't picking the strongest model, but a learning loop that compounds. Plus a warning: don't let a few models eat every industry.

Dimillian (an iOS dev now at OpenAI) wrote a field guide for Codex Mobile. The part worth keeping is a mental model that holds across tools: your phone is not a shrunken terminal, it is the control center that keeps you making decisions while the agent does the work.

Two of the most senior AI engineers alive said the same thing this week: stop prompting your agent, design loops that prompt it for you. Loop engineering unpacked — open vs closed loops, the six building blocks, prompt vs loop engineer. Plus: spotting one smooth ad sewn into the lesson.

Vaibhav Srivastav's Codex prompt is interesting because it describes an agent maintenance loop: look back at recent work, find repeated workflows, and package only high-confidence patterns into Skills, automations, or subagents. It is agent dreaming: turning busy work into capability.

OpenAI's Cookbook frames Codex Goals as a thread-scoped completion contract: the objective persists, but completion must be checked against evidence. This post fills in the official spec angle around SP-192, SP-197, and SP-207.

Codex `/goal` is not a wish machine. Chris Hayduk's real point is engineering discipline: give the agent a measurable finish line, a fast feedback loop, and Markdown files that work as long-term memory.

LLMs are not gods, and they are not just tools. They are more like DOTA teammates: great at last-hitting, occasionally great at feeding. The human job is not to fight AI for the same lane, but to cover taste, map awareness, context ownership, and strategic judgment.

Thariq explains why HTML is replacing Markdown in Claude Code workflows: not as prettier output, but as readable, operable, shareable artifacts that keep humans inside the agent decision loop.

A context window is a model's day: how many lessons, messages, tool results, and task events Ryland can experience before sleep, compression, or collapse.

Jarrod Watts looked inside Codex Goals and found that it solves early stopping, not long-run drift. The real long-running agent stack needs upfront clarification, multi-agent review, and memory outside the context window.

Anthropic's Claude Dreams is not just summarization. It gives agents an offline memory-consolidation loop: reread old memories and up to 100 past sessions, then produce a fresh, auditable memory store.

OpenClaw's automation docs put scheduled work, background tasks, Heartbeat, Hooks, Standing Orders, Task Flow, and related mechanisms on the same map. Task Flow is the layer for multi-step flow state, sync, and revision tracking; this piece reads those boundaries conservatively.

Anthropic accidentally shipped the full TypeScript source code of Claude Code CLI inside an npm source map. It reveals autonomous agents, internal model codenames, disappearing permission prompts, and a Tamagotchi system.

A Tsinghua Shenzhen team proposes Natural-Language Agent Harnesses: move agent control logic from code into structured language executed by an IHR runtime. Harnesses can reshape behavior, but more structure does not always mean better results.

Artificial Analysis launches AA-AgentPerf, a hardware benchmark that uses real coding agent trajectories instead of synthetic queries. It allows production optimizations, measures per-accelerator/per-kW/per-dollar efficiency, and scales from single cards to full racks.

Anthropic launches Claude Code Channels with native Telegram and Discord support, turning Claude Code into a 24/7 always-on AI agent. VentureBeat calls it the OpenClaw killer.

Claude Computer Use sparked huge excitement, with many claiming AI will fully replace human workers. But the original author points out that while AI can handle technical operations, it can't replace human judgement and cultural context. The real moat is still deep domain knowledge.

Andrew Ng announces a new course on A2A (Agent2Agent Protocol). With IBM's ACP merging in, A2A is becoming the industry standard for agent-to-agent communication, letting you connect Google ADK and LangGraph agents seamlessly.