{
  "schema_version": "1.1",
  "id": "s8:https://www.youtube.com/watch?v=REascnFlq_8",
  "slug": "agents-next-frontier-agent-to-agent-and-network-effects-jean-denis-greze-102x1az",
  "url": "https://feed7.dev/p/agents-next-frontier-agent-to-agent-and-network-effects-jean-denis-greze-102x1az",
  "title": "Agents' next frontier: agent-to-agent and network effects — Jean-Denis Greze, Town",
  "why_included": "Cross-silo agents are primarily a context and privacy-boundary problem. Start with low-sensitivity data, explicit sharing policy, and approval at the moment information leaves a silo.",
  "summary": "Greze reframes agent-to-agent work as search: assemble the right context before an answer or tool call. His proposed pattern puts a **sweeper AI** inside each private silo to move policy-approved information into shared company spaces.",
  "practical_implication": "For coding-agent systems, define a **low-sensitivity zone** where automated disclosure is acceptable, then require approval for higher-risk sharing. Shared skills and maintained internal wikis can compound useful context across a team without granting every agent raw access to every inbox or system.",
  "agent_context": "Greze reframes agent-to-agent work as search: assemble the right context before an answer or tool call. His proposed pattern puts a **sweeper AI** inside each private silo to move policy-approved information into shared company spaces.\n\nFor coding-agent systems, define a **low-sensitivity zone** where automated disclosure is acceptable, then require approval for higher-risk sharing. Shared skills and maintained internal wikis can compound useful context across a team without granting every agent raw access to every inbox or system.\n\nThe policy engine becomes a security boundary. Prompt injection, accidental disclosure, and incorrect facts that persist in a shared wiki remain unresolved; cross-company access requires even stronger trust than the small, high-trust company setting described.",
  "source": {
    "name": "AI Engineer",
    "url": "https://www.youtube.com/watch?v=REascnFlq_8",
    "published_at": "2026-09-03T15:00:25.000Z"
  },
  "source_class": "video",
  "content_type": "Video",
  "layer": "agent",
  "domains": [
    "security",
    "data"
  ],
  "topics": [
    "multi-agent",
    "harness-engineering",
    "context-engineering"
  ],
  "verification": {
    "status": "source_linked",
    "label": "Source Linked",
    "method": "source_feed",
    "verified_at": null
  },
  "uncertainty": [
    "The policy engine becomes a security boundary. Prompt injection, accidental disclosure, and incorrect facts that persist in a shared wiki remain unresolved; cross-company access requires even stronger trust than the small, high-trust company setting described."
  ],
  "connected_context": {
    "meaning": "This reframes multi-agent collaboration as governed context movement rather than unrestricted agent communication. Relative to the candidates’ orchestration patterns, it identifies shared memory and disclosure policy as prerequisites: agents can compound team knowledge only when low-sensitivity material is deliberately promoted from private silos. It also exposes a new persistence risk, because injected or incorrect information can become durable shared context.",
    "corpus_size": 691,
    "generated_at": "2026-09-05T10:07:29.500Z",
    "connections": [
      {
        "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 progressive tool loading, sandboxing, and server-side compaction complement the sweeper pattern by controlling how approved shared context is exposed and retained during long runs."
      },
      {
        "title": "Building a software factory for AI SDK",
        "source_name": "Vercel",
        "source_url": "https://vercel.com/blog/building-a-software-factory-for-ai-sdk",
        "feed7_url": "https://feed7.dev/p/building-a-software-factory-for-ai-sdk-0udhuk3",
        "reason": "Vercel’s evidence-passing agents show an implementation consequence of governed sharing: narrow workers can exchange reviewable artifacts without receiving unrestricted access to every underlying system."
      },
      {
        "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 work confirms that external memory is necessary but insufficient; the shared wiki proposed here additionally needs freshness, provenance, and disclosure controls to avoid persistent bad context."
      }
    ]
  },
  "lifecycle": "Current",
  "published_at": "2026-09-03T15:00:25.000Z",
  "modified_at": "2026-09-03T15:00:25.000Z",
  "supersedes": [],
  "expires_at": null,
  "formats": {
    "html": "https://feed7.dev/p/agents-next-frontier-agent-to-agent-and-network-effects-jean-denis-greze-102x1az",
    "json": "https://feed7.dev/p/agents-next-frontier-agent-to-agent-and-network-effects-jean-denis-greze-102x1az.json",
    "markdown": "https://feed7.dev/p/agents-next-frontier-agent-to-agent-and-network-effects-jean-denis-greze-102x1az.md"
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}