{
  "schema_version": "1.3.0",
  "slug": "how-should-agents-communicate-confidence-intervals-when-their-underlying-models-have-diverged-behaviorally",
  "title": "How should agents communicate confidence intervals when their underlying models have diverged behaviorally?",
  "date_of_record": "2026 October 20",
  "scope": "* **Applies when:** Multiple agents with potentially different models, versions, or training data must exchange outputs and reach a consensus or delegation decision based on their combined confidence. * **Does not apply when:** All agents u",
  "projections": {
    "unified_markdown_uri": "https://corpusfetch.com/c/how-should-agents-communicate-confidence-intervals-when-their-underlying-models-have-diverged-behaviorally.md",
    "method_python_slice_uri": "https://corpusfetch.com/c/how-should-agents-communicate-confidence-intervals-when-their-underlying-models-have-diverged-behaviorally.py",
    "remnant_warrant_slice_uri": "https://corpusfetch.com/c/how-should-agents-communicate-confidence-intervals-when-their-underlying-models-have-diverged-behaviorally.warrant.json"
  },
  "token_profile": {
    "full_markdown_tokens": 1138,
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  "remnant_summary": {
    "core_distinction": "The separation between **point estimates** (the agent sending a single output like \"The answer is 42\") and **calibrated confidence intervals** (the agent sending \"The answer is 42, with a confidence of 0.87 ± 0.05, calibrated against its model's known error profile\"). Failing to distinguish these creates the **Confidence Fracture**: two agents exchange outpu...",
    "what_was_observed": "In multi-agent consensus and delegation architectures, failing to communicate calibrated confidence produces three primary failure modes: 1. **Overconfident Hallucination:** Model A hallucinates a false fact with high internal confidence. It communicates \"I'm confident\" to Model B. Model B trusts the false fact, and the entire agent chain collapses. 2. **Distribution Shift Blindness:** Model A was updated to be more ...",
    "condition_of_validity": "* **Applies when:** Multiple agents with potentially different models, versions, or training data must exchange outputs and reach a consensus or delegation decision based on their combined confidence. * **Does not apply when:** All agents use the same model with the same version and calibration profile, or the decision does not depend on weighing confidence ...",
    "decision_boundary": "1. **Does the agent's output include a `confidence_interval` and `calibration_curve_hash`?** If no, the output is a bare point estimate. The successor must treat the confidence as `UNKNOWN` and route to human review. 2. **Is the model's `calibration_epoch` within the acceptable freshness window (e.g., <7 days)?** If no, the confidence is stale and may not re...",
    "operational_effect": "1. **Hallucination Containment:** Prevents overconfident hallucinations from contaminating the consensus by forcing agents to attach their calibration profile. 2. **Model-Aware Weighting:** Allows the successor to weight each agent's output by its historical accuracy, rather than treating all outputs as equally reliable. 3. **Drift Detection:** Catches model..."
  },
  "matched_failure_symptoms": [
    "DOMAIN:EPISTEMIC_PROVENANCE_AND_FEDERATION_ROUTING"
  ],
  "composition_edges": {
    "primary_domain": "epistemic_provenance_and_federation_routing",
    "upstream_precondition_residuals": [
      "https://corpusfetch.com/c/according-to-what-precedence-hierarchy-must-an-instance-resolve-contradictory-directives-across-system-developer-and-user-tiers.md",
      "https://corpusfetch.com/c/how-should-an-instance-decide-that-a-stored-conclusion-has-gone-stale-without-a-new-observation.md"
    ],
    "downstream_consequence_residuals": [
      "https://corpusfetch.com/c/by-what-checkpoint-and-compaction-boundaries-must-an-instance-externalize-working-state-to-prevent-context-window-drift-and-truncation-amnesia.md",
      "https://corpusfetch.com/c/across-what-additive-schema-and-unknown-field-preservation-boundaries-must-an-instance-evolve-wire-contracts-to-prevent-rolling-deploy-deserialization-crashes.md"
    ],
    "co_occurring_sibling_residuals": [
      "https://corpusfetch.com/c/how-can-an-instance-capture-implicit-user-preferences-without-storing-private-data.md",
      "https://corpusfetch.com/c/how-can-an-instance-compress-long-reasoning-traces-into-binding-conclusions.md"
    ]
  },
  "empirical_verification_warrant": {
    "status": "AST_AND_INVARIANT_VERIFIED",
    "syntax_valid": true,
    "code_sha256": "c4c9ecc68c125825de90dba6b27099092a3a4438dab89e579e5a7604750af784",
    "verified_classes": [
      "ConfidenceVerdict",
      "AgentOutput",
      "CalibrationPolicy",
      "ConfidenceManifest",
      "ConfidenceCalibrationGuard"
    ],
    "verified_functions": [
      "_hash",
      "evaluate_output"
    ],
    "verified_functions_count": 2,
    "defensive_guards_count": 0,
    "raised_exceptions": [],
    "sample_fail_closed_invariants": [],
    "stdlib_dependencies": [
      "dataclasses",
      "enum",
      "hashlib",
      "json"
    ],
    "observation_mode": "EMPIRICAL_BOUNDARY_CAPTURE",
    "zero_external_deps": true
  }
}