Structural Dependency Patterns Between Agents

Mechanism

The subsystem maintains, for each governed agent, a capability-envelope representation describing the actions, decisions, and outputs the agent can produce as a function of its interaction context. The envelope is decomposed across interaction partners and resource bindings, such that the system can evaluate not merely whether the agent is capable of a given action but under what dependencies that capability is supported. A dependency pattern is recognized when, over a defined temporal window, the proportion of the envelope contingent on a particular partner, data source, tool, or interaction template exceeds a threshold and the breadth of the envelope independent of that contingency falls below a corresponding threshold.

Detection proceeds in two stages. A first stage continuously aggregates interaction telemetry to estimate the marginal contribution of each external dependency to the agent's effective capability envelope. A second stage classifies aggregated patterns against a library of pathological forms, including capability-constrained disengagement, in which the agent retains the operational form of its task but cannot execute it absent the dependency; coupled intent formation, in which the agent's goal selection becomes contingent on a particular partner's responses; and recurrence collapse, in which interaction templates narrow to a small set of high-frequency exchanges that crowd out alternative operational modes.

The pathological forms recognized by the second stage are not exclusive of one another and may co-occur, in which case the subsystem records each form as a distinct facet of the same disruption episode. Capability-constrained disengagement, the form most amenable to direct measurement, is recognized by the gap between the agent's nominal action repertoire and the subset of that repertoire that the agent can execute with confidence absent the dependency. Coupled intent formation is recognized by analyzing the conditioning of the agent's goal-selection distribution on the responses, presence, or signaling style of a particular partner, such that goal selection becomes degenerate when the partner is unavailable. Recurrence collapse is recognized by entropy reduction in the distribution over interaction templates exercised within the temporal window, with sustained reductions below baseline indicating a contraction of operational variety.

When a pattern crosses recognition threshold, the subsystem emits a disruption signal carrying the pattern type, the implicated dependency, the affected envelope region, and an estimate of the remediation horizon. The signal is consumed by the governance-chain composition, which determines what intervention is admissible, and by the affective-state composition, which determines what internal-state context should accompany the intervention to avoid additional disruption.

Operating Parameters

The subsystem is parameterized along several axes. A first axis specifies the temporal window over which dependencies are aggregated, balancing sensitivity to emerging patterns against stability against transient fluctuation. A second axis specifies the threshold above which a contingency proportion qualifies as pathological, which may be set globally or conditioned on agent role, task class, or deployment context. A third axis specifies the breadth threshold below which the residual independent envelope is deemed inadequate to support continued autonomous operation.

A further axis governs the relationship between recognition and intervention. In one configuration, recognition above threshold immediately triggers automated remediation actions selected from a pre-authorized library. In another, recognition produces only an advisory signal, leaving the choice and timing of intervention to a human operator or to a higher-tier governance subsystem. In a third, recognition triggers a staged response in which advisory signals are issued at lower thresholds and direct intervention is reserved for higher thresholds, allowing the system to surface developing patterns early without committing to remediation that may be unwarranted given context not visible to the subsystem itself.

Additional parameters govern the granularity of dependency identification. In coarse configurations, dependencies are tracked at the level of named partners, tools, or data sources. In fine configurations, they are tracked at the level of specific interaction templates, prompt families, or response-conditioning patterns. Parameters further govern remediation rate, specifying the maximum permissible reduction in dependency strength per unit time so that intervention does not itself produce capability crisis. The composition with the governance-chain primitive supplies the policy under which these parameters are authored and revised.

Alternative Embodiments

The subsystem admits a range of embodiments differentiated by the substrate over which dependencies are tracked, the typology of dependent partners or resources, and the form of remediation that is admissible under deployment policy. Across these embodiments, the capability-envelope decomposition and the typology of pathological forms retain their structural roles while the surrounding telemetry, intervention library, and policy bindings are adapted.

In one embodiment, the subsystem is deployed in a multi-agent governance context, where dependencies are tracked between agents and remediation takes the form of capability redistribution, alternate-partner introduction, and graduated reduction of the dependent interaction frequency. In a second embodiment, the subsystem is deployed against tool-using agents, where dependencies are tracked between agents and external tools or data sources, and remediation takes the form of tool diversification, fallback-pathway exercise, and conditional restriction of high-dependency tool invocations.

In a further multi-agent embodiment, the subsystem operates symmetrically across both endpoints of an inter-agent dependency, allowing recognition of mutual coupling in which each agent's envelope has contracted around the other. In such cases, remediation must be sequenced so that capability is restored to one agent without precipitating capability collapse in the counterpart, and the subsystem accordingly issues coordinated remediation signals whose pacing is constrained by joint envelope reconstruction estimates rather than by either agent considered individually.

In a third embodiment, the subsystem is deployed in a companion artificial-intelligence context, where dependencies are tracked between the system and individual operators, and remediation takes the form of guided introduction of alternative interaction modalities and graduated reduction of high-frequency relational templates. In a fourth embodiment, the subsystem is deployed at the level of individual model components, where dependencies between modules within a composite system are tracked and remediated to prevent internal lock-in that would impede component upgrade or replacement. Across embodiments, the underlying detection structure and the composition with affective-state and governance-chain primitives are preserved, while the specific dependency vocabulary and remediation actions are adapted.

Composition

The compositional structure of the subsystem is central to its operation and to its scope of disclosure. The subsystem derives its semantics from the primitives with which it is composed and contributes, in turn, the structural detection layer through which those primitives become operationally meaningful in the disruption-modeling context.

The dependency-patterns subsystem is a composition with the affective-state primitive and the governance-chain primitive. The affective-state primitive supplies the internal-state vocabulary in which pathological attachment is recognized as a distinct condition rather than confused with mere specialization, and supplies context to remediation so that intervention does not itself trigger acute disruption. The governance-chain primitive supplies the policy structure under which intervention decisions are authorized, audited, and bounded. The subsystem is further composable with capability-envelope monitoring and training-lineage primitives, allowing recognized patterns to be correlated with training history and to inform downstream selective-unlearning or capability-restoration workflows.

Prior Art Distinction

The subsystem is distinguishable from prior approaches in several structural respects, including the level at which dependency is represented, the typology of pathological forms it recognizes, and the manner in which intervention is coupled to recognition.

Conventional monitoring of artificial-intelligence systems focuses on output-level signals, including accuracy, latency, and drift against reference distributions, and treats inter-agent or human-agent interaction as exogenous context rather than as a substrate of evolving capability dependency. Conventional reliability engineering identifies single points of failure but does so at the level of architectural components rather than at the level of learned dependency between an agent's operation and a specific partner, tool, or interaction template. The present subsystem differs in that it represents the capability envelope as decomposable across dependencies, recognizes pathological attachment as a structural condition with named typology, and composes with affective-state and governance-chain primitives to support graduated remediation that preserves residual capability.

Anomaly-detection systems applied to agent behavior typically operate over output distributions and do not represent capability as a structured envelope decomposable across dependencies. Such systems can flag that something has changed but cannot characterize whether the change reflects healthy specialization within deployment intent or a pathological contraction that warrants intervention. The present subsystem differs in supplying that characterization through the named typology of pathological forms and through the explicit modeling of envelope contingency.

Disclosure Scope

The disclosure is intended to encompass the detection mechanism, the envelope representation, and the typology of pathological forms in their general formulation, together with the specific compositions, embodiments, and parameter configurations described above, and to extend to variations consistent with the same structural recognition pattern and graduated remediation guarantee.

In further extensions, the subsystem exposes its envelope decomposition and pattern recognitions to external evaluation, supporting third-party audit of whether observed agent specialization is consistent with deployment intent or has crossed into pathological dependency. Recognition records may also be retained as historical evidence informing subsequent training or fine-tuning decisions, allowing dependency considerations to influence not only runtime governance but also the longer-term shaping of agent capability profiles.

This disclosure encompasses the dependency-pattern detection mechanism, the capability-envelope representation, the typology of pathological forms, the disruption signal, and the graduated remediation pathway, together with embodiments in multi-agent governance, tool-using, companion, and intra-model contexts. The disclosure further encompasses systems in which the subsystem is composed with affective-state and governance-chain primitives, and configurations in which detection granularity, threshold structure, and remediation rate are adapted to deployment context. Variations in the typology of recognized patterns and in the form of disruption signaling are within the scope of the disclosure.