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06:00

Super-agent Design: Router/Worker Split for Always-On Agents

Router + Worker:把 Agent 做成永远在线的超级智能体

The DeepSeek Harness team applies classic systems ideas to agent architecture. A long-lived Router session handles all human interaction and task dispatch but never executes work; context-isolated Workers run tasks concurrently and subscribe directly to event Threads, so follow-up events bypass the Router. Environment events (Triggers) are filtered by code fast paths before any model call, the context window is treated as a cache with Inline/Spill plus multi-hop Chain of Recall, and an external Watchdog session checks whether Workers are actually making progress. In a synthetic ablation, the two-level design finished tasks at p50 12.4 / p95 15.7 minutes versus 20.4/29.5 for a single session with a subagent tool; removing pass-through raised Router cache-read tokens 37%. The authors flag synthetic workloads and unresolved cloud/endpoint security.

06:00

Choosing a Claude model: dial effort before you switch tiers

Claude 模型怎么选:先调 effort,再换档位

Anthropic's own guide to picking among Haiku 5.5, Sonnet 5.5, Opus 5.5 and Fable 5.2. The argument: adjust effort level before switching models, because a higher tier at a lower effort is often cheaper per finished task than a smaller model at max. Evidence is concrete — Opus 5.5 at high beat Fable 5.1 at max on Terminal-Bench 3.0 (58.9% vs 58.0%) using half the tokens; on FrontierCode 2.0 Fable 5.2 at high roughly matched Opus 5.5 at max (45.3% vs 45.5%) at about half the cost per task. On a SWE-bench Pro subset, running everything at low and re-running only failures at high reached roughly 97% for $0.17 per task, against 95.3% for $0.29 with everything at high. Also covers cache-read economics, preserved-thinking rules when swapping models, subagent YAML, and migration gotchas that return 400s. For engineers building agents or budgeting token spend.

claude.dev · 20 min · Claude · Cost Optimization · LLM · Multi-Agent · Prompt Caching
06:00

Four-Layer Agent Stack: Prompt vs Context vs Loop vs Harness

Agent 四层栈:先判断坏在 Prompt、Context、Loop 还是 Harness

A long-form X post that separates four commonly conflated terms into one nested stack: the harness executes loops, each loop iteration assembles context, and context carries the prompts. For each layer it lists the unit of work, typical artifacts, and failure symptoms, plus a diagnostic table: quality decay after turn 15 means prune history (context), repeated identical edits mean missing memory and no-progress detection (loop), double writes and hung subprocesses mean idempotent retries and timeouts (harness). The headline claim: if verification never runs, no prompt edit helps. It cites Karpathy on context engineering, Boris Cherny on loops, and a LangChain Deep Agents Terminal-Bench 2.0 gain from harness changes alone — without numbers. Useful as a layered triage checklist for engineers shipping production agents.