Planning Graph Archival for Cognitive Forensics

Mechanism

Planning graph archival for cognitive forensics is defined in Chapter 4 of the cognition patent as a structural component of the forecasting engine's persistence layer. The mechanism does not store summary records or sampled traces. Every planning graph that the forecasting engine instantiates, evaluates, prunes, promotes, or abandons is committed to a lineage chain whose nodes are content-addressed and cryptographically linked to their predecessors. The chain is append-only at the data structure level, not by policy convention.

Each archival entry records the canonical inputs that gave rise to a planning graph, the policy reference under which the graph was constructed, the bounded depth and branching parameters in force at construction time, the evaluation outcomes for each branch, and the disposition assigned by the lifecycle controller. Because the inputs are themselves anchored to prior lineage entries, the entire causal antecedent of any decision is recoverable by following the chain backward. The forensic property is not a feature of an external log shipped alongside the agent; it is a property of the data structure the agent already operates on.

Reconstruction proceeds by selecting a lineage anchor that corresponds to a moment of interest, dereferencing it, and walking the predecessor edges until the investigator has assembled the closed set of inputs, policies, and intermediate evaluations that produced the agent's behavior. Because every node is content-addressed, tampering anywhere in the chain invalidates the anchor. Because every node is reachable from at least one anchor that the system has already issued, no portion of the planning history can disappear without leaving a detectable break.

Operating Parameters

The archival mechanism is governed by three parameter classes declared in the policy reference. The retention horizon establishes the minimum interval over which lineage entries must remain dereferenceable. The anchor cadence governs how frequently consolidated anchors are issued; tighter cadences reduce the cost of forensic reconstruction at the expense of additional anchoring overhead. The granularity setting controls whether intermediate evaluation steps within a single planning graph are committed individually or as a batched archival entry; the choice trades storage volume against the resolution of post-hoc analysis.

Every parameter is read from the policy reference at construction time and re-read on policy revision events. Parameter changes do not retroactively alter prior archival entries; instead, the lineage chain records the parameter transition itself, so that investigators can reason about which retention and granularity rules were in force at any moment of interest. This avoids the failure mode in which a policy change silently invalidates the forensic record of earlier behavior.

Performance budgets are also declared in policy. The forecasting engine refuses to commit a planning graph whose archival cost would violate the configured budget; the refusal is itself recorded as a lineage entry, preserving the principle that the chain is never silently incomplete. This contrasts sharply with conventional logging, where backpressure typically results in dropped records and a corresponding loss of forensic continuity.

Alternative Embodiments

In a single-agent embodiment, the lineage chain is maintained in local storage with anchors published periodically to an external attestation service. The attestation service does not store the planning graphs themselves; it stores only the anchor digests, which are sufficient to detect tampering without exposing the agent's internal deliberation to third parties. This embodiment is appropriate for autonomous vehicles and embedded systems where bandwidth to a central authority is constrained but the operator must be able to demonstrate that the agent's decision history has not been altered after the fact.

In a multi-agent embodiment, the lineage chains of cooperating agents are cross-anchored. When one agent acts on a planning graph that depends on another agent's output, the dependent agent's lineage entry incorporates the producing agent's anchor by reference. The composite chain therefore captures inter-agent causality without requiring a centralized scheduler. Forensic reconstruction in this embodiment can follow causal edges across agent boundaries, supporting investigation of emergent multi-agent behavior that would be invisible to per-agent logs.

In a regulated embodiment, anchors are co-signed by an external compliance authority at the cadence required by the governing regulation. The co-signature does not give the authority access to the underlying planning graphs; it only fixes the agent's claim about its own state at the moment of signature. Subsequent disputes about agent behavior are resolved by reference to the co-signed anchors, eliminating the need for the authority to maintain a parallel logging infrastructure or to trust the operator's unilateral records.

In a redacted-disclosure embodiment, the lineage chain supports selective dereference under cryptographic commitment. An investigator presented with a sensitive incident can verify that a particular anchor exists and that a particular branch belongs to the chain rooted at that anchor, without the operator disclosing branches unrelated to the investigation. The structural property that makes this possible is content addressing: every branch is independently dereferenceable, and presence is verifiable without exposing siblings. This embodiment supports investigations that must respect commercial confidentiality or third-party privacy without sacrificing forensic integrity.

Composition

The archival mechanism composes with the forecasting engine's bounded planning graph construction. Because graphs that violate construction bounds are rejected before they enter the lineage chain, the archive is not polluted by structurally invalid speculation. It composes with the inference control layer's pre-generation distinction: source provenance for substrate-derived versus inference-generated content is preserved across archival, so forensic reconstruction can answer not only "what did the agent consider" but also "where did each input originate."

The mechanism composes with policy revision controls. When a policy change is enacted, the chain records both the prior and revised policy references, so that an investigator can determine whether a particular planning graph was constructed under the same governance regime that the operator currently asserts. This supports the regulatory pattern in which an operator must demonstrate that historical behavior was compliant with the rules then in force, rather than retroactively justified under later rules.

Composition with cryptographic anchoring services is intentionally generic. The lineage chain treats the anchoring service as an opaque commit oracle. Operators may substitute one anchoring backend for another without altering the structural guarantee, provided the substitute meets the declared properties of append-only commitment, content addressing, and tamper-evident dereference.

Prior Art

Conventional agent observability relies on application logs, structured tracing, and post-hoc telemetry pipelines. These approaches share a common limitation: the log is a derivative artifact emitted alongside the agent's primary computation, and it is therefore subject to backpressure, sampling, log rotation, and operator discretion. None of these systems provide a structural guarantee that the recorded history is the same history the agent acted on. Cognitive forensics, in the sense established by the cognition patent, requires that the forensic record be the agent's own working substrate, not a parallel narrative.

Distributed tracing systems such as those built on the OpenTelemetry data model provide structured causality across services, but they capture only the spans that instrumentation chose to emit. The trace records what the operator decided to observe, not what the agent decided to consider. A trace can be made arbitrarily detailed without ever capturing the rejected planning branches that distinguish a near-miss from a system that did not perceive the hazard. The distinction matters precisely in the cases that motivate forensic review.

Blockchain-based audit trails provide tamper-evidence for transaction sequences but are not designed to capture the rich, branching structure of a forecasting engine's deliberation. They typically record committed actions, not the considered alternatives that were rejected. A forensic investigation that can only see what was done, and not what was contemplated and discarded, cannot distinguish a system that narrowly avoided a harmful action from one that never considered it.

Reinforcement learning replay buffers preserve trajectories for training purposes but are not anchored, are typically lossy, and are not retained beyond the training horizon. They do not support adversarial forensic review. The mechanism described here differs in that the retention property is structural, the anchoring property is cryptographic, and the granularity captures the full planning graph rather than the action sequence alone.

Forensic Workflow

A forensic investigation begins with the selection of an anchor that brackets the moment of interest. The anchor is dereferenced against the attestation service to confirm that it has not been altered since issuance. The investigator then walks the predecessor edges from the anchored entry, materializing the planning graphs that were active at the moment of interest along with their construction parameters, evaluated branches, and lifecycle dispositions. Because each predecessor edge is content-addressed, the investigator can detect any attempt to substitute a falsified history; the substitution would produce a different content address and therefore a different anchor.

The walk is bounded by the inputs the investigation requires. To answer "what did the agent know at moment T," the walk follows input edges until it reaches the substrate-derived entries that grounded the active planning graph. To answer "what alternatives did the agent consider," the walk enumerates the rejected branches recorded under each lifecycle event. To answer "under what policy was the decision made," the walk follows policy reference edges to the version of policy that was in force at construction time. The same chain answers all three questions because all three causal classes were committed structurally, not as separate logs that might have diverged.

Investigations that span multiple agents follow cross-anchored edges across the agents' respective chains. The investigator does not require unified logging infrastructure; each agent's chain is independently verifiable, and the cross-anchors fix the inter-agent causality at the moments at which it occurred. This supports investigation of multi-agent incidents in which the harmful behavior emerged from interaction rather than from any single agent's local decision.

Disclosure Scope

The disclosure covers the construction of an append-only lineage chain whose entries are content-addressed planning graphs, the cryptographic anchoring of that chain to external attestation, the policy-governed parameterization of retention and granularity, the recording of construction-bound rejections and policy transitions as first-class lineage entries, and the cross-anchoring of multi-agent chains to capture inter-agent causality. It covers the use of the chain as the authoritative substrate for cognitive forensics, including the structural guarantee that no log gap exists between what the agent considered and what investigators can later inspect. The scope extends to embodiments in autonomous vehicles, companion AI, therapeutic agents, and enterprise systems, and to any agent architecture in which the forecasting engine's deliberation is required to remain reconstructable after the fact.