The Complete Thirteen-Stage Mutation Lifecycle

Mechanism

A mutation proposal originates either from an internal learning loop, from an externally injected operator directive, or from a peer agent's recommendation. The proposal carries a structured payload describing the model component to be modified, the new parameter values or rule, the reason hash, the proposing principal, and the policy reference under which the proposal was generated. The payload is canonicalised and hashed, and the resulting digest becomes the mutation identifier carried through every subsequent stage.

Stage one, stimulus reception, captures the proposal and time-stamps it against the agent's monotonic clock. Stage two, intent evaluation, checks whether the proposal is consistent with the agent's currently declared intent set. Stage three, context integration, attaches the relevant memory and environmental context to the proposal so that downstream stages reason against the same evidentiary base. Stage four, forecasting evaluation, runs the proposed mutation through a forecasting engine that estimates the distribution of outcomes the mutation would produce on a representative workload sample. Stage five, affective modulation, applies the agent's current affective state to the forecast, raising or lowering effective evaluation thresholds.

Stage six, confidence assessment, computes a scalar confidence value and a confidence interval over the forecast outcome. Stage seven, integrity check, compares the proposed post-mutation behavior against the agent's integrity manifest and flags any deviation. Stage eight, capability verification, confirms that the agent retains the capabilities required to execute under the proposed mutation. Stage nine, the admissibility gate, makes a binary determination informed by all preceding stages and by the current governance policy. A negative determination terminates the lifecycle with a rejection record. A positive determination promotes the mutation to bounded canary deployment.

Stage ten, execution or delegation, runs the mutation against a fenced canary slice of the live workload while the unmutated model continues to serve the bulk of traffic. Stage eleven, outcome recording, captures observed behaviour, error counts, latency, and any governance breach signals during the canary window. Stage twelve, coherence verification, compares observed outcomes to the forecast distribution and computes a coherence score. Stage thirteen, lineage commitment, atomically commits either the admit decision or the reject decision into the agent's append-only lineage log, and on admit replaces the live model component with the mutated version.

Each stage emits a structured event whose payload includes the mutation identifier, the stage identifier, the inputs consulted, the output produced, the principal under whose credentials the stage executed, and the digest of the previous stage's event. The chain of digests forms a per-mutation hash chain that is in turn anchored at lineage commitment time into the agent's global lineage Merkle tree. Tampering with any stage event after the fact is detectable because it would invalidate the chain of digests rooted in the global tree.

Operating Parameters

The lifecycle is parameterised by the canary slice fraction, the canary window duration, the forecasting engine's sample size, the confidence-interval width threshold required for admission, the integrity deviation threshold, the coherence score threshold, and the maximum number of mutations admissible per unit wall-clock time. Each parameter is bound to the active governance policy and is read at proposal-receipt time so that all stages of a given mutation see a consistent parameter snapshot.

The canary slice fraction is constrained to a value small enough that a worst-case mutation cannot produce a fleet-level outage in the canary window. The canary window duration is constrained to be long enough to observe at least a policy-defined minimum number of representative interactions. The maximum admission rate is constrained so that a runaway proposal source cannot exhaust the lineage log or the canary capacity.

The forecasting engine's sample size is constrained on the lower end so that confidence intervals remain meaningful and on the upper end so that admission latency does not exceed the policy-defined responsiveness budget for proposal classes that must be processed promptly. Different proposal classes may carry different sample-size bounds within the same governance policy.

Time-stamps at every stage are drawn from the agent's monotonic clock and are cross-referenced against an external time source at lineage commitment so that audit replay can correlate the mutation lifecycle with external events. The hash function used for stage digests and the Merkle tree is bound to the policy and is rotatable; rotation itself is recorded as a special-class lineage event so that auditors can determine which hash function applies to any historical mutation.

Alternative Embodiments

The thirteen stages may be collapsed for low-risk mutation classes by skipping forecasting, affective modulation, or canary deployment, provided that the policy explicitly permits the collapse for the declared mutation class and that the lineage record carries a tag identifying the collapse profile applied. High-risk mutation classes may conversely be required to carry additional stages, such as a peer-attestation stage in which a quorum of sibling agents must counter-sign the forecast before admission.

The forecasting engine may be implemented as a learned surrogate model, as a symbolic simulator, or as a replay engine that re-executes a recorded workload trace against the proposed mutation. The admissibility gate may be a deterministic threshold function, a learned classifier whose weights are themselves credentialed, or a human-in-the-loop approval step.

The canary deployment may be realized as traffic splitting at the workload entry point, as a shadow-execution scheme in which the mutated model runs alongside the live model and outputs are compared without being acted on, or as a temporally bounded full-fleet deployment with automatic rollback. In all embodiments the bounded character of the canary is preserved: the mutation never reaches global effect without crossing the coherence verification stage.

The lineage anchor may be a local hash chain, a Merkle tree rooted in a per-agent commitment, a federated commitment shared across a fleet, or a public anchor written to an external timestamping authority. The disclosure contemplates each of these and combinations thereof.

Composition With Other Mechanisms

The mutation lifecycle composes with the entropy-governed valence stabilizer at stage five, where the clamped valence reading drives the affective modulation step. It composes with the integrity manifest at stage seven, where the manifest provides the reference behavior against which deviation is measured. It composes with the capability registry at stage eight, where the registry enumerates the capabilities the agent currently holds.

The lineage log produced by the lifecycle is the same log consumed by external auditors, by the agent's own self-reflection routines, and by sibling agents that wish to evaluate whether to mirror an admitted mutation. Because every stage event is anchored, an auditor reading the log months after the fact can reconstruct exactly which evidence each stage consulted and exactly which credentials each stage executed under.

Composition with multi-agent governance is direct: a peer-attestation stage may be inserted between coherence verification and lineage commitment so that fleet-level consistency is enforced before any single agent admits a mutation that would diverge it from its siblings.

The lifecycle also composes with rollback machinery. Because the admit decision at stage thirteen records the prior model component digest alongside the mutated component digest, a subsequent governance event may revert the agent to the prior component by issuing a counter-mutation whose target is the digest pair. The counter-mutation traverses the same thirteen stages, so that rollback is not a privileged escape from governance but an ordinary mutation whose direction happens to be backward in the lineage graph.

Prior-Art Distinction

Continuous-deployment pipelines for software services contemplate canary releases, automated rollback, and signed artefacts, but operate over deployable software units rather than over behavior-model parameters of an autonomous agent and do not anchor each pipeline stage's evidence into a per-mutation hash chain. Online-learning systems contemplate parameter updates with safety constraints, but typically apply the constraints as a regulariser at training time rather than as a discrete admissibility gate with auditable lineage.

Prior work on policy-gradient methods with trust-region or proximal updates bounds the magnitude of a single update but does not produce a per-update audit trail nor distinguish between proposal, review, canary, and admission as named stages. Prior work on ML model governance contemplates approval workflows for trained model versions but does not extend the workflow to in-flight behavioural mutations of a running agent.

The novelty claimed here lies in the integration of all three: a named-stage lifecycle, a bounded canary stage that is mandatory for non-collapsed mutation classes, and per-stage cryptographic anchoring into a lineage that is both auditable and replayable.

Distributed-ledger approaches to model provenance contemplate signing trained model artefacts but typically anchor only the artefact, not the chain of evaluation evidence that justified its admission. The lifecycle described here anchors the evidence as well as the outcome, so that admission can be challenged on grounds of insufficient or improperly credentialed evaluation rather than only on grounds of an incorrect final decision.

Workflow-based human approval systems offer named stages and audit trails but do not contemplate automated bounded canary deployment as an integral stage and do not provide cryptographic anchoring strong enough to detect retroactive insertion or deletion of stage events. The lifecycle described here treats canary deployment and cryptographic anchoring as load-bearing structural requirements rather than optional augmentations.

Disclosure Scope

This disclosure covers the named thirteen-stage lifecycle, the canonicalisation and hashing of mutation proposals, the per-stage event format and its hash-chain construction, the global lineage Merkle tree, the policy-bound parameter set governing canary fraction and admission thresholds, the collapse profiles for low-risk mutation classes, the augmentation profiles for high-risk classes, and the composition of the lifecycle with affective stabilization, integrity manifests, capability registries, and peer-attestation. The disclosure extends to any embodiment in which a behaviour-model mutation of an autonomous agent passes through proposal, review, bounded canary, and admit-or-reject stages with each stage anchored cryptographically into an append-only lineage.