Affective State as a Deterministic Control Primitive for Semantic Agents

Introduction: Affect as Control, Not Narrative

In most software systems, control is expressed through explicit parameters such as timeouts, retry limits, risk flags, and thresholds. In most psychological framing, affect is expressed as subjective feeling. A cognition-native execution substrate requires a different control abstraction: a structured state that modulates how an agent evaluates options and commits to action while remaining deterministic, governable, and subordinate to policy.

In this architecture, affective state is not a story about why an agent acts. It is a bounded control field that changes how the agent behaves under uncertainty, novelty, and constraint. It influences which candidates are considered viable, how aggressively the agent explores alternatives, how quickly it abandons failing paths, and how strict promotion criteria must be before execution is permitted.

This definition is intentionally non-clinical. Affective state is introduced here as a platform primitive that later supports higher-level cognitive models, including computational psychiatry.

1. The Primitive: A Structured Modulation Layer

Affective state is represented as a structured vector or object attached to a semantic agent, with fields that modulate evaluation and selection behavior. These fields are not emotions. They are control parameters that shape the agent’s internal dynamics, such as sensitivity to uncertainty, tolerance for ambiguity, appetite for novelty, persistence under partial failure, and escalation behavior under time pressure.

The key requirement is separation of concerns. Affective state may influence how strictly an agent evaluates candidates, but it does not itself validate truth, confer permission, or bypass policy. Affective state can change the shape of deliberation, but it cannot create authority.

This makes affect useful in governable systems. It provides a principled way to tune behavior without turning the tuning mechanism into an implicit decision-maker.

2. What Affective State Modulates

In cognition-native execution, agents routinely generate candidate actions, candidate responses, candidate delegations, and candidate state mutations. Affective state modulates the evaluation and promotion of those candidates. Under one affective configuration, the agent may require high confidence and strong support before promoting a candidate. Under another configuration, the agent may favor rapid exploration, tolerate uncertainty, and attempt more alternatives before converging.

Typical modulation targets include promotion thresholds, search breadth, branch growth rates, decay rates for unpromoted candidates, escalation thresholds for requesting additional context, and persistence parameters that govern how long partially successful strategies remain active.

Importantly, these are not ad-hoc “personality knobs.” They are execution controls that can be reasoned about, tested, and bounded, enabling reliable tuning across deployments.

3. Determinism, Traceability, and Auditability

Affective state is designed to be explicit and inspectable. Changes to affective state are recorded as state mutations with lineage, so that observers can determine when and why an agent’s control posture shifted. This prevents hidden drift, silent escalation, or unexplained behavioral changes that would otherwise make autonomous systems untrustworthy.

Determinism does not imply rigidity. It means that given the same state, the same inputs, and the same policies, the agent’s modulation behavior can be explained and reproduced within defined tolerances. This is essential for high-stakes governance, debugging, and compliance.

4. Policy-Bounded Affect

Because affective state changes behavior, it must be governed. The system treats affective state updates as policy-bounded mutations: only certain fields may change, within defined ranges, and under admissible circumstances. Policies may restrict escalation behavior, limit risk tolerance, enforce minimum validation strictness, or require additional verification before certain affective transitions are allowed.

This prevents a common failure mode in autonomous systems: permitting an internal control state to evolve into an unsafe posture without an auditable authorization path. Affective state can therefore be tuned for environment, domain, and safety requirements while remaining bounded by enforceable rules.

This division of labor is deliberate. Inference systems may assist by proposing candidate updates or summarizing observations, but governance determines whether an affective update is admissible before it takes effect.

5. How Affective State Updates Are Derived

Affective state may be updated based on structured observations such as repeated failure patterns, conflict between competing objectives, time pressure, novelty exposure, or uncertainty levels derived from model confidence and evidence quality. The system treats these observations as inputs to an explicit update rule rather than as reasons to allow opaque drift.

Updates may be gradual or event-driven, and they may include decay or recovery dynamics that return an agent to baseline when conditions normalize. When updates are permitted, they occur through explicit mutation records so that downstream behavior can be attributed to a known control posture rather than an unexplained shift.

6. Relationship to Forecasting and Integrity Primitives

Affective state is orthogonal to the other cognition-native primitives that follow in this series. Forecasting mechanisms define how candidate futures are generated and structured, while integrity mechanisms define how deviation is bounded and re-verified. Affective state does not replace either primitive. It modulates how aggressively the system explores, how strictly it promotes candidates, and how it responds under uncertainty, but it does not define the structure of forecasting or the rules of integrity.

Keeping these primitives separate is essential. It allows affect to be tuned without changing forecasting logic, and it allows integrity guarantees to remain stable even as control posture shifts.

Conclusion: A Governable Control Layer

Affective state, in a cognition-native execution substrate, is a deterministic control layer that shapes how semantic agents evaluate, prioritize, persist, and escalate within policy-bounded limits. It is introduced here as execution infrastructure: a governable modulation mechanism that affects deliberation dynamics without asserting cognitive meaning, emotional interpretation, or independent authority.

The next primitives in this series formalize forecasting and integrity as independent structures, enabling cognitive models to be composed from separable, governable components rather than from monolithic narratives.