Struct DriftMeter 

pub struct DriftMeter {
    pub agent_a: AgentId,
    pub agent_b: AgentId,
    pub state_divergence: f64,
    pub causal_divergence: f64,
    pub context_divergence: f64,
    pub composite_score: f64,
    pub threshold: f64,
    pub is_drifting: bool,
    pub computed_at: Timestamp,
}

Composite divergence metric between two agents.

All scores are in [0, 1]: 0.0 = identical, 1.0 = fully diverged.

Fields

agent_a: AgentId

First agent in the comparison.

agent_b: AgentId

Second agent in the comparison.

state_divergence: f64

State-level divergence (0 = same state, 1 = fully different).

causal_divergence: f64

Causal divergence from DAG lane comparison.

context_divergence: f64

Context overlap divergence (1 - Jaccard similarity of scored items).

composite_score: f64

Weighted composite score.

threshold: f64

Threshold from IntentDef.drift_threshold (default 0.85). Drift is detected when 1.0 - composite_score < threshold, i.e. when composite divergence is high enough.

is_drifting: bool

Whether the agents are considered to be drifting apart.

computed_at: Timestamp

When this measurement was taken.

Implementations

impl DriftMeter

pub fn compute(input: DriftInput<'_>) -> Self

Compute the drift between two agents from their event streams.

This is a pure function — no I/O.

fn compute_state_divergence(a: &AgentState, b: &AgentState) -> f64

Compare two AgentState values for divergence.

Returns 0.0 if identical variant, 0.5 if same category but different details, 1.0 if completely different states.

fn compute_causal_divergence( events_a: &[Event<Value>], events_b: &[Event<Value>], decay: &DecayParams, ) -> f64

Compare DAG lane sequences between two agents.

Uses the ratio of shared event kinds in recent history as a proxy for causal alignment. Apply Weibull decay to weight recent events more.

fn decay_weighted_kind_histogram( events: &[Event<Value>], decay: &DecayParams, ) -> HashMap<u16, f32>

Build a decay-weighted histogram of event kinds.

fn compute_context_divergence(items_a: &[Uuid], items_b: &[Uuid]) -> f64

Compute context divergence as 1 - Jaccard similarity.

Trait Implementations

impl Clone for DriftMeter

fn clone(&self) -> DriftMeter

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl ComposeSchema for DriftMeter

fn compose(generics: Vec<RefOr<Schema>>) -> RefOr<Schema>

impl Debug for DriftMeter

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for DriftMeter

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl PartialEq for DriftMeter

fn eq(&self, other: &DriftMeter) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Serialize for DriftMeter

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl ToSchema for DriftMeter

fn name() -> Cow<'static, str>

Return name of the schema.

fn schemas(schemas: &mut Vec<(String, RefOr<Schema>)>)

Implement reference [utoipa::openapi::schema::Schema]s for this type.

impl StructuralPartialEq for DriftMeter