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06:01

Introducing Cloudflare Agents: Agent Tracing and Observability

Cloudflare Agents 上线:先解决 Agent 可观测性

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 · Observability
06:00

Introducing routines: scheduled cloud automations in Claude Code

Claude Code 新增 routines:云端定时/API/Webhook 触发自动化

Anthropic is adding routines to Claude Code in research preview. A routine packages a prompt, a repo, and connectors into an automation that runs on Anthropic's cloud, so nothing depends on your laptop staying open. There are three triggers: a schedule (hourly, nightly, weekly), an HTTP endpoint with its own auth token that returns a session URL, and GitHub repository webhooks that open a session per matching PR and keep feeding in follow-ups like comments and CI failures. The post sketches usage patterns—nightly backlog triage, docs drift scans, post-deploy smoke checks, Datadog alert triage—and documents daily caps: 5 routines for Pro, 15 for Max, 25 for Team/Enterprise, with extra usage billed beyond. Existing /schedule CLI tasks migrate automatically. Note this is a vendor launch post: it defines the feature surface but offers no benchmarks or independent evaluation.

06:00

Messages, System Prompts and Reasoning Tokens

LLM 消息协议入门:系统提示、推理令牌与工具调用

This article from AI Hero offers a beginner-friendly breakdown of the message protocol used when talking to LLMs: user messages, assistant messages, system prompts, reasoning tokens, file attachments, and tool calls. It argues that a system prompt placed at the start of history usually wins when it conflicts with a user instruction, though jailbreaks show the rule is not absolute. Reasoning tokens are treated as part of the assistant message rather than a separate layer, and messages can have multiple parts so files can be sent or returned. A short todo.md example illustrates how a tool call with an id is answered by a tool result carrying the same id, turning tools into a conversation between the LLM and your application. That loop is what powers apps like Claude Code and Cursor. Useful for engineers who want a clear mental model before building LLM-based products.

www.aihero.dev · 3 min · Agents · AI Engineering · Context Engineering · LLM