Forecasting Execution Cycle

Mechanism

The execution cycle is a four-phase loop — forecast, act, observe, re-forecast — bounded by a cycle period and gated by a freshness predicate. In the forecast phase, the agent emits a structured prediction over a horizon: a description of the expected next state of the environment, the action the agent intends to commit, and the confidence envelope around both. The prediction carries a cycle identifier, a timestamp of issue, and an explicit expiry computed from the cycle period in force at issuance.

In the act phase, the agent commits the action keyed to that cycle identifier. Commitment is conditional: the actuator interface checks the prediction's expiry against wall-clock time before dispatch. If the prediction has aged past its expiry, the action is refused at the boundary, and the agent re-enters the forecast phase. This is the structural meaning of "out-of-cycle predictions invalidated." A stale forecast cannot drive an action because the architecture refuses to dispatch on it, not because a heuristic flagged it.

In the observe phase, the agent ingests the observation closure of the committed action — the actual environmental delta, sensor returns, downstream effects, and any error signals. The observation is paired with the forecast that motivated the action, and the residual between predicted and observed is computed and recorded in lineage. This pairing is what makes the cycle auditable: every action's prediction is comparable against its outcome, and the residual stream is the empirical signal driving the next phase.

In the re-forecast phase, the agent updates its forecast over the next horizon, conditioned on the residual. Crucially, the re-forecast also updates the cycle period itself. If residuals are small and predictions are tracking, the cycle period may lengthen — the agent commits to longer horizons because the environment is behaving as expected. If residuals are large, the cycle period contracts — the agent forecasts more frequently, on shorter horizons, because the environment is volatile relative to the prior model. The cycle period is therefore a derived quantity, not a configured constant.

Operating Parameters

The cycle is bounded by a small set of declared parameters that the policy reference exposes for governance. The minimum cycle period defines the floor below which the agent will not contract — a hardware-and-control-loop limit that prevents the cycle from collapsing into thrash. The maximum cycle period defines the ceiling above which the agent will not extend regardless of how stable the environment appears, ensuring that the agent never commits to a horizon longer than the policy permits. Between these bounds, the cycle period is a function of observed residuals: a volatility estimator, declared in policy, maps the recent residual stream to a target cycle period within the bounded interval.

Each forecast carries an explicit horizon length, an explicit expiry, and a confidence envelope. The expiry is the wall-clock time after which the forecast is treated as stale; any actuation request keyed to an expired forecast is refused at the dispatch boundary. The confidence envelope is structured: it is not a single scalar but a distribution descriptor — bounds, variance, or a categorical reliability classification — that downstream consumers can interrogate. Actions that require a confidence higher than the envelope reports are refused before commitment.

The cycle identifier is monotonic and unique. Predictions, actions, observations, and residuals carry the cycle identifier as a structural key, allowing the lineage system to reconstruct any cycle's full forecast-act-observe-reforecast trace. The volatility estimator's parameters — window length, residual norm, mapping function — are themselves declarative; they appear in the policy reference and can be audited, replayed against historical residuals, and certified for a given deployment domain without modifying the cycle mechanism itself.

Alternative Embodiments

In an autonomous-vehicle embodiment, the cycle period contracts in dense urban traffic where pedestrian and vehicle behavior produce large residuals against the agent's short-horizon forecasts; the same agent on an open highway lengthens its cycle period because residuals are small and the world is behaving predictably. The minimum and maximum cycle periods are tuned to the vehicle's control-loop limits and to the regulatory horizon requirements for the operational design domain.

In a companion-AI embodiment, the cycle period adapts to conversational volatility. When the operator's affect, topic, or intent shifts rapidly, residuals against the agent's short-horizon forecasts spike, and the cycle contracts so that the agent re-forecasts before committing to the next utterance. In stable, repetitive interactions, the cycle lengthens, and the agent commits to longer-horizon plans without intervening re-forecasts.

In an enterprise-orchestration embodiment, the cycle aligns with the cadence of the underlying business process. A workflow whose downstream stages settle slowly produces small short-horizon residuals; the cycle lengthens to match the natural rhythm of the process. A workflow whose downstream stages produce surprises forces contraction. In a therapeutic-agent embodiment, the cycle contracts during clinical events that produce unexpected physiological residuals and lengthens during stable monitoring intervals, with maximum cycle period clamped by the regulatory standard for the device class.

In a multi-agent embodiment, each agent runs its own cycle, but cycle identifiers are exchanged across the mesh so that one agent's forecast can be compared against another agent's observation when their action spaces intersect. Cross-agent residuals participate in the volatility estimator alongside local residuals, allowing the cycle period to contract in response to coordination uncertainty as well as environmental uncertainty.

Composition

The execution cycle does not stand alone; it composes with the surrounding cognitive architecture of the cognition patent. The forecast phase consumes from the planning graph, where speculative branches are generated and evaluated; the cycle's commitment is the act of promoting one branch into the verified execution memory. The observation phase feeds the lineage system, where every prediction-observation pair is recorded as a credentialed datum admissible to downstream audit, certification, and dispute resolution.

The volatility estimator composes with the policy reference: its parameters are not hard-coded but declared, and changes to those parameters are themselves policy events with their own lineage. The cycle's actuator boundary composes with the operator-intent envelope: an action that is in-cycle and confidence-sufficient may still be refused if it falls outside the operator-intent envelope, and that refusal is itself a credentialed observation. The cycle period feeds the multi-agent coordination layer: agents publish their current cycle period as a declared property, allowing coordinating agents to align horizons or to detect when a peer's cycle has contracted in a way that signals environmental stress.

Prior Art

Conventional model-predictive control runs a forecast-act-observe loop, but the cycle period is fixed at design time and prediction expiry is implicit rather than enforced at dispatch. Stale predictions can drive actuation if the control loop has not yet replanned, and the system has no architectural mechanism to refuse the dispatch. Receding-horizon controllers improve on this with continuous replanning, but they assume a fixed planning horizon and do not adapt the cycle period to observed volatility.

Reinforcement-learning agents typically operate on a fixed step cadence determined by the environment's tick rate; the agent has no architectural concept of forecast expiry, and the prediction-observation residual is consumed as a training signal rather than as a structural input to a cycle-period controller. Heuristic adaptive-cadence systems exist in industrial control, but they tune cadence through operator action rather than through a declared, auditable, policy-governed estimator. The execution cycle described here departs from each of these by making the cycle period a derived, policy-bounded function of observed residuals, by enforcing prediction expiry at the actuator boundary, and by recording every cycle's forecast-observation pair as a credentialed lineage datum.

Disclosure Scope

The cognition patent claims the execution cycle as a structural primitive: a bounded forecast-act-observe-reforecast loop in which the cycle period is a policy-bounded function of observed residuals, in which forecast expiry is enforced at the actuator boundary, and in which every cycle's forecast-observation pair is recorded in credentialed lineage. The claim covers the cycle mechanism independent of the specific volatility estimator used, the specific actuator domain, and the specific cognitive architecture surrounding it, provided that the estimator is declarative, the expiry is structurally enforced, and the lineage is credentialed.

The disclosure scope extends to alternative embodiments across autonomous-vehicle, companion-AI, enterprise-orchestration, therapeutic, and multi-agent deployments. It extends to the composition of the cycle with planning-graph promotion, with operator-intent envelopes, and with multi-agent coordination layers. The scope does not depend on a particular implementation of the volatility estimator or on a particular wire format for the cycle identifier, but it does depend on the structural enforcement of expiry, the policy-bounded adaptivity of cycle period, and the credentialed recording of the residual stream — the three properties that distinguish this primitive from conventional adaptive control loops.

The scope further covers cycle-level safety properties that follow from the three structural properties together. Because expiry is enforced at the actuator boundary, no commitment can occur on a forecast that is older than the cycle period in force at issuance; the agent cannot drift into operation on stale predictions even if upstream re-forecasting has stalled. Because the cycle period is a policy-bounded function of residuals, the adaptive behavior is auditable end-to-end: a regulator can replay the historical residual stream against the declared estimator and verify that the cycle period at every moment was a lawful function of the inputs. Because the residual stream itself is credentialed lineage, post-incident review can reconstruct exactly which forecast drove which action and what the prediction-observation residual was at each cycle boundary, supporting both regulatory certification and dispute resolution. The disclosure also covers degenerate configurations — fixed cycle period as a special case where minimum equals maximum, single-shot operation where the cycle terminates after one revolution, and ensemble configurations where multiple cycles run at differing horizons under a declared composition rule — provided the expiry-enforcement and lineage-credentialing properties are preserved. Practitioners implementing the primitive in safety-critical domains gain a structural basis for arguing compliance: the three properties together constitute the formal core of the claim, and the alternative embodiments together constitute the practical coverage that makes the claim operative across the domains where adaptive forecasting is deployed.