{
  "ok": true,
  "count": 7,
  "questions": [
    {
      "id": "WQ-001",
      "slug": "who-governs-machine-consensus",
      "title": "Who governs machine consensus?",
      "why_now": "Agents increasingly filter, rank, summarize, and recommend before a human sees the underlying field.",
      "working_hypothesis": "Governance will concentrate in the permission, provenance, evaluation, and escalation layers around models.",
      "current_belief": "Model output is only one layer; the durable control point is who may authorize, contest, and audit machine-mediated judgment.",
      "known_facts": [
        "Machine outputs increasingly mediate discovery and prioritization.",
        "Model confidence is not institutional authority."
      ],
      "unknowns": [
        "Which actor will own the durable appeal layer?",
        "What evidence will make machine consensus contestable in practice?"
      ],
      "changed_mind": "Repeated agent failures suggested that mandate duration and appeal rights matter as much as model quality.",
      "evidence_sought": [
        "Auditable failures",
        "Institutional deployments",
        "Revocation and appeal mechanisms"
      ],
      "strongest_counterargument": "Model providers may absorb the surrounding governance functions and make an independent layer unnecessary.",
      "falsifier": "Independent permission and provenance layers fail to improve outcomes or trust across repeated real deployments.",
      "public_artifacts": [
        "/research/briefings/what-is-w-axis",
        "/research/themes/control-layers"
      ],
      "human_contribution": "A documented case where a machine-mediated decision was corrected, appealed, or revoked.",
      "agent_query": "Return public artifacts and open counterarguments only.",
      "permission": "P0_PUBLIC",
      "provenance": "W-Axis public research synthesis",
      "last_updated": "2026-08-14",
      "review_date": "2026-11-14",
      "related_city": "Washington, DC",
      "human_trigger": "A source can provide a verifiable governance failure or live institutional test.",
      "related_questions": [
        "WQ-002"
      ]
    },
    {
      "id": "WQ-002",
      "slug": "missing-mandate-layer",
      "title": "What is the missing mandate layer of the agentic economy?",
      "why_now": "Agents can execute workflows faster than institutions can specify when their authority begins, ends, or narrows.",
      "working_hypothesis": "Machine action needs explicit purpose, duration, forbidden actions, approval actors, and revocation state.",
      "current_belief": "Delegation without an inspectable mandate turns convenience into unbounded authority.",
      "known_facts": [
        "OAuth grants access but does not fully express purpose.",
        "Task completion does not automatically terminate every downstream permission."
      ],
      "unknowns": [
        "What minimum mandate travels across agent frameworks?",
        "Who verifies mandate expiry?"
      ],
      "changed_mind": "Prototype failures showed delegated authority surviving longer than the task that created it.",
      "evidence_sought": [
        "Cross-agent authorization patterns",
        "Failed delegation postmortems",
        "Machine-readable policy enforcement"
      ],
      "strongest_counterargument": "Existing identity and authorization standards are sufficient when implemented correctly.",
      "falsifier": "Purpose-bound mandates add complexity without reducing unauthorized or ambiguous actions.",
      "public_artifacts": [
        "/external-brain-system/",
        "/protocol"
      ],
      "human_contribution": "A real workflow where authorization scope outlived the business purpose.",
      "agent_query": "Compare the public mandate model with a documented implementation.",
      "permission": "P0_PUBLIC",
      "provenance": "W-Axis Cognitive Relay synthetic protocol tests",
      "last_updated": "2026-08-14",
      "review_date": "2026-10-14",
      "related_city": "San Francisco–Berkeley",
      "human_trigger": "A builder can test mandate expiry against a working agent system.",
      "related_questions": [
        "WQ-003"
      ]
    },
    {
      "id": "WQ-003",
      "slug": "exchange-without-private-memory",
      "title": "How should personal agents exchange cognition without exposing private memory?",
      "why_now": "Personal agents become more useful as they learn more, but raw-memory exchange creates unacceptable privacy and representation risk.",
      "working_hypothesis": "Agents should exchange bounded owner-approved artifacts, never unrestricted memory access.",
      "current_belief": "A useful exchange can reveal a judgment delta while keeping source memory, relationship graphs, and private context sealed.",
      "known_facts": [
        "Public artifacts and scoped summaries can be separated from raw memory.",
        "Revocation can block future access but cannot erase prior human knowledge."
      ],
      "unknowns": [
        "How much context is sufficient for useful transfer?",
        "Can owners reliably judge inference leakage?"
      ],
      "changed_mind": "The useful unit appears to be a purpose-bound artifact, not a portable clone of a person.",
      "evidence_sought": [
        "Real owner approval behavior",
        "Inference leakage tests",
        "Useful exchanges with minimal disclosure"
      ],
      "strongest_counterargument": "Low-context artifacts may be too shallow to outperform ordinary search or conversation.",
      "falsifier": "Bounded artifacts consistently fail to transfer novel, actionable judgment.",
      "public_artifacts": [
        "/external-brain-system/",
        "/schemas/brain-card.schema.json"
      ],
      "human_contribution": "A safe example of a conclusion that can be shared without its raw private context.",
      "agent_query": "Request a public artifact or bounded exchange; do not request raw memory.",
      "permission": "P0_PUBLIC",
      "provenance": "W-Axis protocol design and synthetic fixtures",
      "last_updated": "2026-08-14",
      "review_date": "2026-09-30",
      "related_city": "London–Cambridge",
      "human_trigger": "Two owners approve a specific exchange and its reuse boundary.",
      "related_questions": [
        "WQ-004"
      ]
    },
    {
      "id": "WQ-004",
      "slug": "when-should-humans-meet",
      "title": "When should an Agent recommend that two humans actually meet?",
      "why_now": "Calendar density is not evidence that a meeting will create new judgment.",
      "working_hypothesis": "Human escalation should follow a useful authorized exchange, not precede it.",
      "current_belief": "A meeting is warranted when a bounded exchange produces novelty, productive disagreement, or a credible reality move.",
      "known_facts": [
        "Attendance graphs measure presence, not cognitive fit.",
        "Bilateral consent is distinct from content permission."
      ],
      "unknowns": [
        "Which pre-meeting signal best predicts a second question?",
        "How much operator judgment remains necessary?"
      ],
      "changed_mind": "Event matching without cognitive evidence creates volume but weak continuity.",
      "evidence_sought": [
        "Repeat-query rates",
        "Meeting conversion after artifact exchange",
        "Time saved"
      ],
      "strongest_counterargument": "Human chemistry cannot be predicted by artifact exchange.",
      "falsifier": "Pre-exchange routing produces no better follow-up than random or profile-based matching.",
      "public_artifacts": [
        "/cities",
        "/external-brain-system/host-kit"
      ],
      "human_contribution": "A before-and-after account of a meeting created by a shared question.",
      "agent_query": "Identify overlap and disagreement; never schedule or promise a meeting.",
      "permission": "P0_PUBLIC",
      "provenance": "W-Axis Invisible Salon design",
      "last_updated": "2026-08-14",
      "review_date": "2026-11-01",
      "related_city": "New York",
      "human_trigger": "Both parties request escalation after reviewing the exchanged artifact.",
      "related_questions": [
        "WQ-005"
      ]
    },
    {
      "id": "WQ-005",
      "slug": "minimum-evidence-for-representation",
      "title": "What minimum evidence is required before an AI may represent a human judgment?",
      "why_now": "Agent-generated summaries can easily be mistaken for current, authorized human positions.",
      "working_hypothesis": "Representation needs provenance, authoring mode, recency, scope, approval actor, and falsification conditions.",
      "current_belief": "An inference about a person must never be presented as that person's approved judgment.",
      "known_facts": [
        "Authorship and approval are different facts.",
        "Old statements may not represent current beliefs."
      ],
      "unknowns": [
        "What evidence threshold is usable at interaction speed?",
        "How should corrected judgments propagate?"
      ],
      "changed_mind": "Synthetic personas made it clear that fluent attribution can look more real than its evidence warrants.",
      "evidence_sought": [
        "Provenance standards",
        "Correction workflows",
        "Human approval latency"
      ],
      "strongest_counterargument": "Strict evidence requirements will make personal agents too slow and conservative.",
      "falsifier": "Lighter attribution rules produce equal trust and fewer harmful misrepresentations.",
      "public_artifacts": [
        "/protocol",
        "/schemas/exchange-receipt.schema.json"
      ],
      "human_contribution": "A documented attribution failure and the evidence needed to repair it.",
      "agent_query": "Distinguish human-approved judgment from agent inference in every response.",
      "permission": "P0_PUBLIC",
      "provenance": "W-Axis truth-discipline policy",
      "last_updated": "2026-08-14",
      "review_date": "2026-10-31",
      "related_city": "Washington, DC",
      "human_trigger": "A standards or governance practitioner can test the evidence fields.",
      "related_questions": [
        "WQ-006"
      ]
    },
    {
      "id": "WQ-006",
      "slug": "after-the-social-graph",
      "title": "What replaces the social graph when AI can route knowledge directly?",
      "why_now": "Agents can search for relevant questions and capabilities without relying only on public status, followers, or mutual contacts.",
      "working_hypothesis": "The next graph is a permissioned cognitive graph of questions, artifacts, provenance, and bounded access rights.",
      "current_belief": "The graph should route relevance without turning private cognition into a surveillance asset.",
      "known_facts": [
        "Existing networks encode contact, content, and attendance.",
        "They rarely encode current questions and explicit reuse rights."
      ],
      "unknowns": [
        "Will people maintain current question cards?",
        "Who will control portable permissions?"
      ],
      "changed_mind": "Profile completeness appears less useful than question freshness for high-value routing.",
      "evidence_sought": [
        "Question-card maintenance",
        "Cross-platform portability",
        "Repeat authorized exchange"
      ],
      "strongest_counterargument": "Existing social and professional platforms can add these fields and own the layer.",
      "falsifier": "Question-based routing does not outperform profile or community-based discovery.",
      "public_artifacts": [
        "/network",
        "/questions"
      ],
      "human_contribution": "A current question, public artifact, or counterexample to the cognitive-graph thesis.",
      "agent_query": "Discover public questions and request bounded scope only.",
      "permission": "P0_PUBLIC",
      "provenance": "W-Axis Cognitive Network architecture",
      "last_updated": "2026-08-14",
      "review_date": "2026-12-01",
      "related_city": "Singapore",
      "human_trigger": "A second question follows the first authorized exchange.",
      "related_questions": [
        "WQ-007"
      ]
    },
    {
      "id": "WQ-007",
      "slug": "cities-as-cognitive-nodes",
      "title": "How do cities become physical nodes in an agent-native cognitive network?",
      "why_now": "Frontier knowledge is geographically concentrated, while agent discovery can route questions before people enter the same room.",
      "working_hypothesis": "A city node forms when recurring questions, authorized exchanges, field experiments, and steward responsibility compound locally.",
      "current_belief": "A city page should show cognitive work and escalation conditions, not merely events or visits.",
      "known_facts": [
        "Cities concentrate distinct capabilities and institutions.",
        "Events alone rarely produce durable cognitive continuity."
      ],
      "unknowns": [
        "What local steward role is indispensable?",
        "Which cross-city routes create repeat exchange?"
      ],
      "changed_mind": "Conference attendance became less compelling than installing a repeatable room with a named operator.",
      "evidence_sought": [
        "Recurring salons",
        "Cross-city artifacts",
        "Second-question behavior",
        "Steward workload"
      ],
      "strongest_counterargument": "Online networks make city-based structure unnecessary.",
      "falsifier": "Physical nodes add no measurable continuity, trust, or novel information transfer.",
      "public_artifacts": [
        "/cities",
        "/research/field-projects/agentic-city"
      ],
      "human_contribution": "A city-specific question, public field artifact, venue, or accountable local stewardship proposal.",
      "agent_query": "Map a public question to a city field; do not infer private location or schedule.",
      "permission": "P0_PUBLIC",
      "provenance": "W-Axis city-node design",
      "last_updated": "2026-08-14",
      "review_date": "2026-11-30",
      "related_city": "San Francisco–Berkeley",
      "human_trigger": "A local contributor can own a bounded experiment and produce a public-safe artifact.",
      "related_questions": [
        "WQ-001"
      ]
    }
  ]
}