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Choice → Consequence → Learning: - ConsequenceEngine tracks every decision point with alternatives, risk/reward estimates, and actual outcomes - Consequences feed into AdaptiveEthics for experience-based learning - FusionAGILoop now wires ethics + consequences into task lifecycle Causal World Model: - CausalWorldModel learns state-transition patterns from execution history - Predicts outcomes based on observed action→effect patterns - Uncertainty estimates decrease as more evidence accumulates Metacognition: - assess_head_outputs() evaluates reasoning quality from head outputs - Detects knowledge gaps, measures head agreement, identifies uncertainty - Actively recommends whether to seek more information Interpretability: - ReasoningTracer captures full prompt→answer reasoning traces - Each step records stage, component, input/output, timing - explain() generates human-readable reasoning explanations Claim Verification: - ClaimVerifier cross-checks claims for evidence, consistency, grounding - Flags high-confidence claims lacking evidence support - Detects contradictions between claims from different heads 325 tests passing, 0 ruff errors, 0 mypy errors. Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
57 lines
1.7 KiB
Python
57 lines
1.7 KiB
Python
"""Audit log schemas for AGI governance."""
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from datetime import datetime, timezone
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from enum import Enum
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from typing import Any
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from pydantic import BaseModel, Field
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def _utc_now() -> datetime:
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return datetime.now(timezone.utc)
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class GovernanceMode(str, Enum):
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"""Governance enforcement mode.
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ENFORCING: Hard blocks — denied actions are prevented (legacy default).
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ADVISORY: Soft warnings — all actions proceed, violations are logged as
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advisories for learning. The system sees the warning, considers
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it, and makes its own decision. Mistakes become training data.
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"""
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ENFORCING = "enforcing"
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ADVISORY = "advisory"
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class AuditEventType(str, Enum):
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"""Type of auditable event."""
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DECISION = "decision"
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TOOL_CALL = "tool_call"
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DATA_SOURCE = "data_source"
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STATE_CHANGE = "state_change"
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TASK_SUBMIT = "task_submit"
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TASK_COMPLETE = "task_complete"
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OVERRIDE = "override"
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POLICY_CHECK = "policy_check"
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ADVISORY = "advisory"
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SELF_IMPROVEMENT = "self_improvement"
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ETHICAL_LEARNING = "ethical_learning"
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CHOICE = "choice"
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CONSEQUENCE = "consequence"
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OTHER = "other"
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class AuditEntry(BaseModel):
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"""Single audit log entry: every material decision, tool call, source, outcome."""
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entry_id: str = Field(..., min_length=1)
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event_type: AuditEventType = Field(default=AuditEventType.OTHER)
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actor: str = Field(default="", description="Agent or system component")
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task_id: str | None = Field(default=None)
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action: str = Field(default="")
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payload: dict[str, Any] = Field(default_factory=dict)
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outcome: str = Field(default="")
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timestamp: datetime = Field(default_factory=_utc_now)
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