{
  "schema_version": "1.1",
  "id": "archive:https://www.youtube.com/watch?v=Z-c11pV_uvU",
  "slug": "anthropic-s-cca-exam-as-a-field-guide-for-agentic-engineering-frank-coyl-00nf896",
  "url": "https://feed7.dev/p/anthropic-s-cca-exam-as-a-field-guide-for-agentic-engineering-frank-coyl-00nf896",
  "title": "Anthropic's CCA Exam as a Field-Guide for Agentic Engineering — Frank Coyle, UC Berkeley",
  "why_included": "A walkthrough of Anthropic’s architect exam doubles as a harness checklist: inspect stop reasons, narrow subagent scope, isolate outputs, and compact long-running context.",
  "summary": "The Claude Certified Architect exam presents **six production scenarios** and randomly selects four. Its scenarios cover agent loops, code generation, multi-agent research, developer workflows, CI, and structured extraction across **five assessed domains**.",
  "practical_implication": "Apply the anti-patterns as harness checks: inspect stop reasons before accepting output, give each subagent only its needed tools and evidence, keep full subtask traces out of the primary thread, and compact long sessions around a defined token threshold.",
  "agent_context": "The Claude Certified Architect exam presents **six production scenarios** and randomly selects four. Its scenarios cover agent loops, code generation, multi-agent research, developer workflows, CI, and structured extraction across **five assessed domains**.\n\nApply the anti-patterns as harness checks: inspect stop reasons before accepting output, give each subagent only its needed tools and evidence, keep full subtask traces out of the primary thread, and compact long sessions around a defined token threshold.\n\nThe talk interprets an exam rather than reporting measured engineering outcomes. Its suggested **150,000-token** compaction trigger is an example, not a demonstrated universal threshold; batch execution also trades **50% lower token cost** for delivery within 24 hours.",
  "source": {
    "name": "AI Engineer",
    "url": "https://www.youtube.com/watch?v=Z-c11pV_uvU",
    "published_at": "2026-08-08T16:00:06.000Z"
  },
  "source_class": "video",
  "content_type": "Video",
  "layer": "agent",
  "domains": [
    "coding"
  ],
  "topics": [
    "harness-engineering",
    "subagents",
    "context-engineering"
  ],
  "verification": {
    "status": "source_linked",
    "label": "Source Linked",
    "method": "source_feed",
    "verified_at": null
  },
  "uncertainty": [
    "The talk interprets an exam rather than reporting measured engineering outcomes. Its suggested **150,000-token** compaction trigger is an example, not a demonstrated universal threshold; batch execution also trades **50% lower token cost** for delivery within 24 hours."
  ],
  "connected_context": {
    "meaning": "This converts an architecture exam’s scenarios and failure modes into a practical harness checklist: validate completion signals, minimize subagent authority, prevent trace pollution, and compact deliberately. It reinforces established context and permission patterns but does not validate them empirically; in particular, the token trigger and batch-cost trade-off should be treated as configurable examples rather than defaults.",
    "corpus_size": 419,
    "generated_at": "2026-08-12T10:05:33.238Z",
    "connections": [
      {
        "title": "Evolution of agentic surfaces — Gagan Bhat & Isabella Kai He, Anthropic",
        "source_name": "AI Engineer",
        "source_url": "https://www.youtube.com/watch?v=K0X9QDRkIdg",
        "feed7_url": "https://feed7.dev/p/evolution-of-agentic-surfaces-gagan-bhat-isabella-kai-he-anthropic-0mtshqh",
        "reason": "The warning that model-specific scaffolding can become harmful makes the target’s exam-derived checks and compaction threshold candidates for per-model validation, not permanent universal rules."
      },
      {
        "title": "Chained Recursive Language Models for Multi-Iteration Reasoning",
        "source_name": "arXiv",
        "source_url": "https://arxiv.org/abs/2608.05124v1",
        "feed7_url": "https://feed7.dev/p/2608-05124v1-05haobv",
        "reason": "Chained RLM supplies a concrete implementation of the target’s context-hygiene advice by resetting contexts while preserving only summaries, a blackboard, and inspectable artifacts."
      },
      {
        "title": "Codex, Behind the Harness — Dominik Kundel, OpenAI",
        "source_name": "AI Engineer",
        "source_url": "https://www.youtube.com/watch?v=shRR1e2HXMk",
        "feed7_url": "https://feed7.dev/p/codex-behind-the-harness-dominik-kundel-openai-04cntno",
        "reason": "Codex’s deferred tools, permission review, and server-side compaction are production-oriented counterparts to the target’s recommendations for bounded tools and deliberate context management."
      },
      {
        "title": "Scaling to Long Horizons — Ross Taylor & Chengxi Taylor, General Reasoning",
        "source_name": "AI Engineer",
        "source_url": "https://www.youtube.com/watch?v=2bvtay8wGYI",
        "feed7_url": "https://feed7.dev/p/scaling-to-long-horizons-ross-taylor-chengxi-taylor-general-reasoning-0jwtg4d",
        "reason": "The long-horizon analysis narrows the target’s compaction guidance: reducing context pressure helps, but does not resolve reward design, stale training data, or scheduling constraints."
      }
    ]
  },
  "lifecycle": "Current",
  "published_at": "2026-08-08T16:00:06.000Z",
  "modified_at": "2026-08-08T16:00:06.000Z",
  "supersedes": [],
  "expires_at": null,
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}