/**
 * `behavioralAnalyst` — a DETERMINISTIC analyst (cost.kind = 'deterministic',
 * never calls the LLM). It produces the efficiency/behavioral findings a
 * tolerant agentic analyzer (HALO) re-derives per run inside the model —
 * context bloat, output decay, tool monoculture, missing self-verification —
 * directly from arithmetic over spans (`computeTraceMetrics`).
 *
 * Why it matters: these findings are model-agnostic BY CONSTRUCTION (no model
 * in the loop), so they cannot return 0 on a weak model the way the Ax-RLM
 * does — and they are strictly more reliable than HALO, which spends tokens
 * re-deriving the same numbers and can hallucinate the trend. The agentic
 * RLM kinds remain for SEMANTIC findings that genuinely need a model; this
 * analyst owns the behavioral class.
 */
import { type BehavioralMetrics } from '../trace-analyst/behavioral-metrics';
import type { TraceAnalysisStore } from '../trace-analyst/store';
import { type Analyst, type AnalystFinding } from './types';
/**
 * Map computed signals → structured AnalystFindings. Pure: no LLM, no clock
 * dependence beyond `produced_at` (overridable for deterministic tests).
 */
export declare function deriveEfficiencyFindings(metrics: BehavioralMetrics, opts?: {
    analystId?: string;
    producedAt?: string;
}): AnalystFinding[];
/** The deterministic behavioral/efficiency analyst (no LLM, any-model). */
export declare function behavioralAnalyst(): Analyst<TraceAnalysisStore>;
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