Primary technical disclosure
Secondary technical
Diagnosing Coping Failure in AI Agents: Coping Intercepts in the Coherence Control Loop How Disruption Modeling (US Application 19/647,395) diagnoses coping failure in autonomous agents as coping intercepts in the coherence control loop: phase-specific interruptions that fire when empathic pressure exceeds affective resilience, each logged and tied to a governed intervention.Three-Domain Integrity Model Integrity tracked across personal, interpersonal, and global domains with independent trajectories, providing fine-grained detection of which normative dimension is deviating.Deviation Function D=(N-T)/(ExS) The deviation function D = (N - T) / (E x S) is a deterministic composite, deviation pressure over deviation resistance, whose continuous output partitions the agent into non-deviation, pre-deviation, and Deviation-Activated states in the integrity architecture.Self-Esteem as Internal Validator Self-esteem is the S(t) term of the deviation function, a deterministic entropy-weighted comparison of the agent's enacted behavior against its declared values that acts as a deviation-resistance counterweight.Deviation as Deterministic Semantic Mutation A deterministic deviation function and a bounded Deviation-Activated State convert constraint pressure into governed, lineage-recorded semantic mutations, with self-esteem and empathy feedback that make further deviation progressively costlier.Integrity Structural Placement Integrity field occupying defined position within agent architecture, interacting with policy, affect, and mutation subsystems through specified interfaces for cross-primitive coupling.Empathy as Distributed Moral Load Bounded modeling of counterparty affective state used for negotiation and harm-minimization, not behavior cloning, with structurally bounded influence on agent policy.Coherence Trifecta Control Loop Three-phase unified control loop of empathy, integrity, and self-esteem executing sequentially for each deviation event to maintain behavioral coherence across all domains.Coping Intercept Patterns Pressure-response mechanisms intercepting the coherence loop at specific phases when pressure exceeds resilience, producing HSP withdrawal, narcissistic externalization, or psychopathic self-esteem collapse patterns.Integrity Deviation Logging Comprehensive deviation log recording every deviation event with sufficient detail to reconstruct complete deviation context, implemented as indexed view of agent lineage.Integrity Collapse Detection Structural breakdown detection of the coherence trifecta where self-correcting feedback mechanisms cease functioning and the agent enters sustained incoherent operation.Redemption Engine Subsystem generating restorative semantic mutations following deviation events through deviation analysis, candidate generation, and restoration impact projection.Moral Trajectory Forecasting Module projecting agent integrity evolution over future time horizons, classifying trajectories into redemption, stabilization, radicalization, and containment archetypes.Integrity-Aware Trust Slope Validation Trust slope validation extended to incorporate integrity trajectory as additional validation dimension, with integrity trust score influencing delegation and governance decisions.Integrity-Confidence Cross-Primitive Coupling Integrity field serving as direct input to confidence-governed execution and forecasting engine, restricting execution authority under compromised integrity conditions.Integrity-Modulated Discovery Traversal Integrity tracking applied to semantic discovery traversal, with a traversal integrity field and a step-by-step semantic drift metric measured against the original query intent.Integrity-Aware Multi-Agent Negotiation Integrity state of each agent influencing weight given to contributions, votes, and proposals in multi-agent collaboration and collective decision-making.Biological Signal Coupling for Integrity Biological signals coupled to interpersonal integrity evaluation, enabling human physiological state to influence agent integrity computations in human-agent interaction.Policy-Based Integrity Constraints Comprehensive policy constraints governing integrity computation, deviation evaluation, coping intercept management, and integrity-based mutation gating.Integrity Field Portability The integrity field, including three-domain scores, deviation function state, self-esteem, empathy weighting, coping state, and the recent deviation log window, is serialized and transmitted during agent migration, then validated against lineage before the receiving substrate admits the agent.Predictive Deviation Alerting Pre-deviation alerts generated when deviation function output approaches activation threshold, triggering preemptive interventions before actual deviation occurs.Governed Forgetting Governed forgetting deprioritizes lineage entries through a policy-defined decay function rather than deleting them: relevance decay, a first-class governance event recorded in lineage, and reversible through governed relevance restoration.Predictive Social Modeling Agent constructing an inferred cognitive state model of another agent from observable behavioral signals, feeding into the forecasting engine to weight planning graph branches for multi-agent coordination.Refusal as First-Class Observation How the integrity subsystem treats the refusal to record a deviation as a recorded coping event, so externalization, minimization, and suppression remain auditable rather than hidden.Historical Policy-Version Reconstruction Forensic reconstruction recovers both a recorded action and the policy in force when it was recorded, because both are immutable lineage entries. Governed forgetting decays relevance weight without removing entries, so reconstruction stays complete.
Applications · general
Autonomous Vehicle Ethical Decision-Making Through Computable Integrity How the integrity and coherence layer of United States Patent Application 19/647,395 applies to autonomous vehicle decision-making: a deterministic three-domain integrity gradient, a deviation function with coping intercepts, and trust-slope validation that keep a driving agent's behavior conformant with its declared safety and ethics profile in real time and reconstructable from a lineage record.Detecting Strategy Drift in Algorithmic Trading Agents With Computable Integrity Algorithmic trading systems optimize for returns within risk limits but have no mechanism to detect when conduct drifts from declared trading principles before a limit is breached. Built on the Integrity and Coherence invention of US Patent Application 19/647,395, computable integrity gives trading agents an integrity field and deviation function that track normative deviation in real time, catching strategy drift, style inconsistency, and ethical boundary violations as they form.How to Keep a Legal AI Agent's Advice Consistent With Precedent and Its Own Prior Positions Build legal advisory AI agents that hold a consistent normative position across related matters, detect when proposed advice contradicts cited authority or the agent's own prior positions, and self-correct before delivery, using the three-domain integrity field, the deviation function, and the coherence trifecta disclosed in US Patent Application 19/647,395.Government AI Policy Agents: Consistency, Equity, and Statutory Alignment by Design How the three-domain integrity model (personal, interpersonal, global) enables government AI policy agents that maintain consistency across departments, detect when recommendations contradict current authority, and keep treatment equitable across constituencies, grounded in United States Patent Application 19/647,395.AI Editorial Agents for Newsrooms: Consistent Standards and Bias-Drift Detection by Design How the three-domain integrity model (personal, interpersonal, global) and the coherence trifecta enable AI editorial agents that hold consistent editorial standards, detect bias and framing drift across coverage, and self-correct before publication, grounded in United States Patent Application 19/647,395.Integrity and Coherence for AI Environmental Compliance Agents: Consistent Regulatory Interpretation Across Facilities and Jurisdictions How the three-domain integrity model enables AI environmental compliance agents that maintain consistent regulatory interpretation, detect when monitoring recommendations contradict established standards, and ensure equitable enforcement across facilities and jurisdictions.Integrity and Coherence for Insurance Underwriting Agents How the three-domain integrity model enables AI underwriting agents that maintain consistent risk assessment criteria, detect discriminatory pricing patterns, and ensure actuarially sound decisions that are equitable across policyholder populations.Integrity and Coherence for Social Media Moderation Agents How the three-domain integrity model enables AI content moderation agents that apply community standards consistently across posts, detect enforcement bias patterns, and maintain equitable treatment across user populations at platform scale.Legal-Evidence Reconstruction for Autonomous-Incident Litigation: Court-Admissible Policy-Version Lineage Courts handling autonomous-incident litigation must reconstruct what the system knew under what governance rules at time T. A cryptographic policy-version lineage, built on the Integrity and Coherence inventive step of US Patent Application 19/647,395, provides that reconstruction as a structural product of the architecture.Regulatory Audit Replay With Historical Policy Versions SEC 17a-4, EU AI Act Article 12, FDA 21 CFR Part 11, GDPR Article 30, and NERC CIP all converge on requiring audit replay that includes the policy in force at the audited time, not just the data. A versioned, credentialed, lineage-resident policy artifact supplies historical policy-version reconstruction that data-only time-travel cannot.When an Agent Drifts and Nobody Can Say When What a delegation confidentiality breach costs a risk operations lead who can date the disclosure but not the deviation behind it, and how a disclosed architecture records deviation at the time it occurs.
Applications · specific
Waymo vs a Governed Integrity Layer for Autonomous Behavior Waymo built the most advanced autonomous driving system in commercial operation, resolving ethical-edge cases per scenario against a configured rule hierarchy. This article positions that architecture against a governed integrity and coherence layer disclosed in United States Patent Application 19/647,395: a multi-domain integrity field, a deterministic deviation function, and graded coping intercepts that give an autonomous agent persistent normative state across decisions.Cruise vs Governed Autonomy: Why AV Safety Needs a Computable Integrity Field Cruise built a sophisticated autonomous vehicle safety framework. This article positions it against the Integrity and Coherence layer of US Patent Application 19/647,395, which specifies integrity as a computable multi-domain field with a deviation function and graded coping response, and explains the architectural axis on which the two differ.JPMorgan Trading Compliance vs Governed Agents: The Integrity Field Gap JPMorgan built sophisticated trading compliance systems that evaluate transactions against regulatory rules and flag statistical anomalies. This article, built on the Integrity and Coherence inventive step disclosed in US Patent Application 19/647,395, examines the architectural difference between per-transaction rule evaluation and a persistent multi-domain integrity field with a deviation function and coping intercepts.Palantir Governance Alternative: Adding a Computable Integrity Field to Government Analytics How the computable integrity field, deviation function, and coping intercepts disclosed in US Patent Application 19/647,395 add continuous normative-drift monitoring on top of Palantir's Gotham, Foundry, and AIP government analytics, and how the two compose rather than compete.Does the Aurora Driver maintain normative memory across decisions? Aurora Innovation builds autonomous trucking and ride-hailing systems with sophisticated perception and planning. But the Aurora Driver does not maintain a persistent normative model that tracks ethical consistency across decisions. This article examines why autonomous systems require integrity coherence as a computational primitive.Nuro Alternative: Governed Autonomous Delivery Beyond Per-Trip Safety Records Nuro builds autonomous delivery vehicles that navigate public roads without human occupants, optimizing delivery efficiency and pedestrian safety per trip. This article contrasts that per-trip architecture with the integrity and coherence layer disclosed in United States Patent Application 19/647,395, which tracks normative consistency across operational decisions through a deterministic deviation function.Zoox vs Governed Autonomy: Tracking Normative Drift the Planner Cannot See Zoox builds a purpose-designed autonomous robotaxi with bidirectional driving and four-wheel steering. The vehicle handles complex urban scenarios but does not maintain persistent normative state across its planning decisions. This article, built on the Integrity and Coherence inventive step disclosed in United States Patent Application 19/647,395, examines why urban autonomous vehicles require a governed integrity field for normative consistency.Motional Alternative for Governed Normative Trajectory in Autonomous Driving Governed normative-trajectory tracking for autonomous driving, built on the Integrity and Coherence inventive step disclosed in United States Patent Application 19/647,395, positioned against Motional's scenario-level safety validation. Per-scenario safety cases verify each situation in isolation; they do not structurally track normative consistency across situations.Argo AI Legacy vs Governed Autonomy: The Missing Deviation Function Argo AI wound down in 2022 despite strong technical talent and billions in backing from Ford and Volkswagen. The company solved hard perception and planning problems within an assurance model built on scenario coverage rather than on an in-loop deviation function that measures behavioral divergence from a declared integrity field. This article, built on the Integrity and Coherence inventive step disclosed in United States Patent Application 19/647,395, examines the architectural axis that separates scenario-coverage assurance from structure-based assurance.comma.ai openpilot and the Governed-Behavior Layer: Integrity Coherence for Learning-Based Driving comma.ai's openpilot learns driving behavior from human demonstrations, producing natural, low-cost Level-2 vehicle control through end-to-end learning. An imitation-learning model, however, does not maintain a separate persistent structure of declared norms, tracked behavior, and a continuous deviation scalar. This article positions the integrity-coherence structure of United States Patent Application 19/647,395 as that governing layer.Apache Iceberg Time Travel vs Governed Replay: Data Without Policy Apache Iceberg time-travel queries reconstruct historical data state across Snowflake, Databricks, and AWS deployments. Snapshot integrity at the storage layer is mature; the governance-binding layer that records and replays which policy versions governed each snapshot is the layer Iceberg does not provide. Built on the Integrity and Coherence inventive step in US Patent Application 19/647,395.
How-to guides
How to Detect When an AI Agent Contradicts Its Own Prior Decisions An architectural how-to for detecting AI agent self-contradiction using an accumulated integrity trajectory and a semantic-dissonance metric, as disclosed in United States Patent Application 19/647,395.How to Keep an AI Agent's Decisions Consistent With Its Own Past Behavior An architectural guide to keeping an AI agent's decisions consistent over time by gating each action against an accumulated integrity trajectory derived from the agent's own history, per US Patent Application 19/647,395.How to Replay and Audit Why an AI Agent Made a Past Decision An architectural approach to deterministically replaying an AI agent's past decision and auditing it against its integrity trajectory, grounded in United States Patent Application 19/647,395.
