/**
 * Proactive prefetch for live-world questions.
 *
 * Why this exists:
 * When a user asks "what is CRCL trading at?", the agent has TradingMarket
 * in CORE and the system prompt demands it be used. The evaluator catches
 * refusals. The auto-retry loop feeds findings back. All four layers run
 * every turn. It still isn't enough — Sonnet 4.6 (the strongest model we
 * route to) confidently answers "Circle is a private company" from 2022
 * training data, refusing the tool across retries.
 *
 * The lesson: every mechanism above depends on the model *agreeing* to call
 * a tool. When the model is confident-but-wrong about current-world state,
 * it doesn't reach for the tool at all. No prompt tweak will fix this —
 * fine-tuning priors beat prompt priors.
 *
 * Harness-level fix: prefetch the data *before* the model decides. When
 * the user's message contains a ticker or a current-events ask, Franklin's
 * harness spends the $0.001 unprompted, injects the result into context,
 * and then the model answers a question it already has evidence for —
 * not a question its training data has a prior about.
 *
 * This is the pattern Anthropic's harness-design writeup calls out:
 * "Remove components that encode a stale assumption (the model will
 * reach for tools on its own), replace with components that handle the
 * coordination gap (harness fetches, model synthesizes)."
 */
import type { ModelClient } from './llm.js';
import type { Dialogue } from './types.js';
import type { MarketCode } from '../trading/providers/standard-models.js';
export interface TickerIntent {
    kind: 'ticker';
    /** Raw symbol as the user wrote it; may be company name or ticker. */
    symbol: string;
    /** Resolved market if the classifier was confident; `us` default when `assetClass === 'stock'`. */
    market?: MarketCode;
    /** Asset class — stock prefers paid Gateway path; crypto stays free on CoinGecko. */
    assetClass: 'stock' | 'crypto';
    /** Does the user also want the news / "why did it move"? */
    wantNews: boolean;
}
export type Intent = TickerIntent | null;
export interface PrefetchResult {
    /** Markdown snippet that gets prepended to the user's message for the LLM. */
    contextBlock: string;
    /** User-visible status line ("*Prefetched CRCL ...*"). */
    statusLine: string;
    /** Spend incurred by prefetch. For telemetry + Markets panel display. */
    costUsd: number;
    /** Did any prefetch call actually succeed? If all failed, the caller may
     *  decide to skip injection entirely and let the model try its own way. */
    anyOk: boolean;
}
/** Run the prefetch for an intent. Concurrent fan-out for price + news. */
export declare function prefetchForIntent(intent: Intent, client: ModelClient): Promise<PrefetchResult | null>;
/**
 * Read an ExaAnswer response through BOTH wire shapes — the live BlockRun
 * gateway returns `{ answer, costDollars }` at the TOP level, while older or
 * proxied deployments nest them under `data`. This mirrors the `res.data ?? res`
 * read in src/tools/exa.ts; this prefetch path is the twin of the ExaAnswer
 * tool and historically drifted out of sync, paying the USDC then dropping the
 * answer. Exported for regression tests. `paid` records whether a real x402
 * charge settled, so a paid-but-empty answer still counts against spend.
 */
export declare function readExaAnswer(raw: unknown, paid: boolean): {
    text: string | null;
    costUsd: number;
};
/**
 * Augment a user message with the prefetch context block prepended. The
 * final model sees the data as part of the "incoming" user turn — no
 * synthetic tool_use fabrication needed, history stays clean.
 */
export declare function augmentUserMessage(originalInput: string, prefetch: PrefetchResult): Dialogue;