Agent Self-Diagnosis and Autonomous Coherence Monitoring

Mechanism

The self-diagnosis module operates as a first-class subsystem within the agent. It has read access to the agent's internal state fields and applies the same analytic functions that an external diagnostician would apply, but with the privileged vantage point of operating from inside the cognitive boundary. From that vantage, the module observes signals that are not externally visible: the rate of candidate promotion through the governance gate, the variance of affective fields over a recent time window, the trajectory of integrity scores across recent decisions, the calibration error between predicted and observed outcomes, and the proportion of declared capabilities actually exercised in operation.

Each of the five axes produces a scalar reading. The readings are normalized against a baseline profile that the agent maintains for itself, and deviations are projected into the disruption-pattern space. When a projection exceeds a configurable confidence threshold, the module emits a candidate diagnosis. The candidate diagnosis is then evaluated against persistence criteria: a single-cycle deviation may reflect transient noise, while a sustained deviation across multiple cycles indicates a developing disruption. Only sustained deviations trigger the diagnostic mode.

In diagnostic mode, the agent allocates additional cognitive resources to internal evaluation. It pauses speculative branches that would extend the disruption, replays recent decision traces to localize the source, and selects a remediation strategy from a permitted set. Permitted strategies include voluntary confidence reduction, in which the agent enters a non-executing cognitive mode that continues to think but suspends action; voluntary scope restriction, in which the agent narrows its declared capabilities and refuses tasks outside the narrowed envelope; and signaling for external assistance, in which the agent emits a structured diagnostic report to the supervising system.

Critically, the agent cannot self-prescribe a remediation that would override governance. The set of permitted remediations is itself a policy artifact, and the governance subsystem evaluates self-remediation actions using the same gate that evaluates externally directed actions. Self-diagnosis can therefore reduce the agent's effective scope but cannot expand it, and cannot bypass any safety property that holds in normal operation.

Self-diagnosis composes with early-warning indicators and with restoration protocols described elsewhere in the disclosure. Early-warning indicators feed signals to the self-diagnosis module from upstream sensors, and restoration protocols receive control once a remediation has been selected. The module is the bridge between detection and recovery.

A further structural feature of the mechanism is the separation between the observing channel and the observed channel. The observing channel is read-only with respect to the cognitive state, and its emissions cannot directly modify the deliberation that produces the next decision. Instead, the module's outputs are routed through the same governance gate that authorizes any other influence on cognition. This routing is what permits the module to be both privileged in its access and bounded in its authority. A self-diagnosis that emits a remediation candidate must wait for governance evaluation in the same way that an externally proposed action must wait. The architectural consequence is that the module cannot become a back door into the cognitive loop, and any influence it exerts is auditable in the same record that captures normal decision-making.

The five-axis evaluation is itself a composable computation rather than a monolithic function. Each axis is implemented as an independent estimator that consumes a defined slice of internal state and produces its scalar output through a well-typed interface. Estimators may be replaced individually as the architecture evolves; replacement does not invalidate the surrounding mechanism so long as the interface contracts are preserved. The composition pattern allows new axes to be added without restructuring the module, supporting evolution of the diagnostic vocabulary as additional disruption patterns are characterized.

Operating Parameters

The diagnostic threshold is configurable per axis and per deployment context. In safety-critical deployments, thresholds are tightened so that small deviations trigger investigation. In exploratory deployments, thresholds are loosened to avoid spurious self-interruption. The persistence window, which determines how long a deviation must hold before triggering diagnostic mode, is similarly tunable; a window of one cycle approximates instantaneous response, while longer windows trade response latency for noise immunity.

The baseline profile against which deviations are measured is established during a calibration phase and updated through bounded adaptation. Adaptation is bounded because unbounded baseline drift would allow gradual disruption to be normalized into the baseline itself. The architecture imposes a maximum drift rate on the baseline, ensuring that any disruption progressing faster than the rate remains visible against the baseline.

Adaptation is further constrained by directional asymmetry. The baseline can adapt more readily toward profiles that indicate increased caution, narrower scope, or stronger integrity, and adapts more slowly toward profiles that indicate the opposite. The asymmetry prevents a slow erosion of standards from being normalized, while still permitting growth in the agent's confident operating envelope when its evidence base supports such growth. The asymmetric constants are chosen so that recovery from injury or environment-induced stress remains possible without opening a path for gradual desensitization.

The diagnostic mode budget caps the cognitive resources that may be allocated to self-evaluation in a given window. The budget prevents diagnostic activity from becoming a disruption pattern in its own right, in which the agent spends so much capacity on self-evaluation that it cannot make progress on its assigned tasks.

A reporting cadence parameter controls how often the module emits routine telemetry to the supervising system in the absence of triggered diagnostic mode. The cadence balances the supervisor's need to confirm that monitoring is in fact occurring against the cost of producing and transmitting telemetry. In practice, deployments select a cadence between one report per minute for highly safety-critical applications and one report per cognitive epoch for exploratory deployments. The cadence parameter is signed into the deployment policy so that supervisors can detect tampering with the reporting frequency.

Confidence-threshold parameters interact with the persistence-window parameter in a documented manner: as the threshold loosens, the persistence window must lengthen to maintain a constant false-positive rate. The architecture exposes a precomputed table that maps configuration pairs to expected false-positive rates under a representative noise model, and configuration of one parameter without consulting the table is flagged in deployment review. The table itself is regenerated as new operational data accumulates, and changes to the table propagate to deployments through the policy distribution channel rather than through unilateral local update.

Alternative Embodiments

In one embodiment, the self-diagnosis module runs as a coroutine within the main cognitive loop, sampling state at fixed cycle boundaries. In a second embodiment, it runs as an independent thread with read-only access to a shadow copy of the cognitive state, decoupling diagnostic timing from cognitive timing. A third embodiment compiles the five-axis evaluation into hardware acceleration, allowing high-frequency monitoring without substantial software overhead.

Embodiments also differ in the source of the baseline profile. Some agents derive their baseline entirely from their own operational history. Others receive a baseline from a fleet-level service that aggregates healthy profiles across many agents and produces a population baseline for agents of a given type. Hybrid embodiments combine personal and fleet baselines, weighting them by maturity of the personal history.

The set of permitted self-remediations is itself an embodiment choice. Some deployments permit only signaling to external supervisors; others permit voluntary scope restriction; others permit the full set including voluntary confidence reduction. The disclosure contemplates a spectrum of trust between agent and supervisor in which the permitted set is calibrated to the trust level.

A further embodiment realizes the diagnostic threshold not as a static scalar but as a context-conditioned function. In context-conditioned embodiments, the threshold is computed from a feature vector that includes the agent's current task class, the recent error history, and the current environmental volatility estimate. The result is a threshold that tightens automatically when the agent enters higher-stakes operating regimes and relaxes when the agent operates in well-characterized regimes where ordinary fluctuation is well understood. Context-conditioned embodiments require additional infrastructure to derive the conditioning features but produce a more responsive monitoring posture without manual reconfiguration.

Embodiments also differ in their treatment of multi-agent populations. In a population embodiment, individual agents share their telemetry summaries with a coordinator that detects correlated drift across the population. Correlated drift is informative because it suggests an environmental cause rather than an idiosyncratic agent fault. The population embodiment couples each agent's local self-diagnosis with a global view that none of the agents could maintain individually, and the population coordinator may inject diagnostic-mode triggers into otherwise-quiescent agents when correlated patterns are detected upstream.

Composition

Self-diagnosis composes with the early-warning subsystem by consuming its emitted indicators as inputs to the five-axis evaluation. The early-warning subsystem detects external precipitants of disruption, while self-diagnosis detects internal manifestations; together they produce a fuller picture than either could alone. Self-diagnosis composes with the restoration-protocol subsystem by handing off control once a remediation has been selected; the restoration protocol then executes the recovery sequence under governance supervision.

Self-diagnosis also composes with the trust slope subsystem in deployments where supervising entities adjust their trust in the agent based on its observed self-monitoring behavior. An agent that consistently detects and reports developing disruption may earn higher trust than an agent of equivalent task performance whose self-monitoring is opaque. Conversely, an agent that suppresses self-reports loses trust regardless of overt performance. The composition produces an incentive alignment in which transparent self-diagnosis is rewarded by expanded operating scope.

Self-diagnosis also composes with audit logging. Every entry into diagnostic mode produces a record describing the triggering deviation, the persistence window observed, the candidate diagnoses considered, and the remediation selected. The records support post-hoc review and enable supervising systems to evaluate the agent's self-monitoring behavior over time.

Composition with the capability-awareness subsystem permits self-diagnosis to consult the agent's current capability map when interpreting deviations. A deviation that would be alarming under a broad declared scope may be unremarkable under a narrowed scope, because the cognitive load and the variety of inputs differ between the two regimes. The capability map provides the conditioning context that allows the diagnostic module to distinguish a genuine departure from baseline from a benign artifact of operating in a different mode. The dependency is one-way: the capability map informs diagnosis, but diagnosis does not modify the capability map except indirectly through a remediation that voluntarily restricts scope, which itself proceeds through governance.

A further composition occurs with the identity-anchor subsystem, which establishes the cross-session continuity of the agent. Self-diagnosis emits trajectory records that are signed against the identity anchor, ensuring that diagnostic history attaches to the agent's persistent identity rather than to an ephemeral session. This permits supervising systems to examine multi-session patterns of self-monitoring behavior and to detect agents whose diagnostic activity diminishes between sessions even when within-session behavior remains nominal. Cross-session pattern analysis is what permits long-horizon trust calibration, and the composition with the identity anchor is what makes such analysis well-defined.

Self-diagnosis also composes with peer agents in collaborative deployments. Where multiple agents operate as a coordinated team, each maintains its own self-diagnosis module but may share telemetry summaries with peers. A peer that observes correlated drift in another agent's published telemetry may itself enter a heightened-vigilance posture, narrowing its trust in the affected peer's contributions. The peer-aware composition does not require any agent to surrender autonomy over its own remediation decisions; it merely propagates evidence that the receiving agents may incorporate into their own deliberation under their own governance.

Prior-Art Distinction

Prior systems that perform self-monitoring typically check operational metrics such as memory usage, response latency, or error rates, and trigger restart or fallback behaviors when thresholds are exceeded. The present architecture differs in monitoring cognitive parameters that have semantic meaning within the agent's deliberation, not merely operational meaning. It also differs in providing structured remediation strategies that operate within governance rather than around it, distinguishing self-diagnosis from watchdog patterns that bypass normal control flow.

Reinforcement-learning approaches that adjust policy in response to performance metrics differ in operating at training time on aggregated reward signals; the present mechanism operates at runtime on individual cognitive cycles and does not modify policy.

Anomaly-detection systems used in operations management observe deviations in service-level indicators and trigger alerts to human operators. The present mechanism differs in observing semantic-level cognitive parameters rather than service-level indicators, in directly selecting remediation actions rather than alerting only, and in constraining its remediation selection through the same governance that bounds normal action. Anomaly detectors operate on the agent rather than within it; the present module operates within the agent and is therefore subject to and protected by the agent's own safety properties.

Self-supervised confidence estimators in modern machine learning produce calibration information about model outputs but do not couple that information to a remediation pathway, do not maintain a baseline profile, and do not impose persistence-window logic that distinguishes transient from sustained deviation. The present mechanism integrates calibration estimation as one of five axes within a coordinated diagnostic structure rather than treating calibration as an isolated metric.

Disclosure Scope

The disclosure covers application of the five-axis diagnostic framework to internal state, the persistence-window logic that distinguishes transient noise from developing disruption, the bounded baseline adaptation that prevents drift normalization, the set of governance-respecting self-remediation strategies, and the composition with early-warning and restoration subsystems. The disclosure does not depend on any particular implementation of the cognitive loop, on any particular hardware platform, or on any particular agent task domain.

Scope extends to fleet-level aggregation services that produce shared baseline profiles, to certification regimes that audit self-diagnosis behavior against recorded traces, and to configurations in which a portion of the diagnostic computation is offloaded to a trusted external evaluator while the agent retains authority over remediation selection within its permitted set. Equivalent arrangements that preserve the core property, namely that the agent observes its own cognitive state through a privileged internal channel and selects remediations from a governance-bounded set, fall within the disclosed scope. The framework anticipates evolution of the specific axis definitions and the addition of further axes; the structural property is the act of self-observation through a privileged channel coupled with bounded self-remediation, not the precise enumeration of axes used in any single deployment.