connectai/src/features/company/dispatcher.ts

/**
 * Sequential dispatcher for 1인 기업 모드.
 *
 * Drives one company "turn":
 *
 *   user prompt
 *      → CEO planner (JSON {brief, tasks})
 *      → for each task in plan: dispatch one specialist (sequentially)
 *           - build specialist prompt (incl. peer context from earlier agents)
 *           - call the AI service
 *           - persist its output to disk
 *           - append its output to the peer-context buffer for the next agent
 *      → CEO reporter (synthesis markdown)
 *      → persist `_report.md`, update agent memory + decisions
 *      → emit `companyTurnUpdate` events to the webview at each phase
 *
 * Why sequential? The user runs Astra on a single GPU/CPU with limited RAM,
 * and parallel agents would force us to keep multiple models loaded
 * simultaneously. Sequential dispatch keeps "exactly one model resident at
 * a time" — the LM Studio lifecycle manager unloads the previous model and
 * loads the next when an agent has its own override.
 *
 * Why not use `AgentExecutor.handlePrompt` here? Because `handlePrompt` is
 * built for the *interactive* chat path: it owns the conversation history,
 * streaming UI, agent-mode injection, and a dozen other things we don't
 * want triggered by a company turn. The company dispatcher needs a clean
 * "one system + one user → one string back" primitive — `AIService.chat()`
 * fits that perfectly. Specialists can still emit action tags
 * (`<create_file>`, `<run_command>`); we route their *raw* output through
 * the existing action-tag executor afterwards so file/command tools work
 * exactly as in chat.
 */
import * as vscode from 'vscode';
import { IAIService } from '../../core/services';
import { logError, logInfo } from '../../utils';
import { getCompanyAgent } from './agents';
import { modelForAgent, readCompanyState } from './companyConfig';
import { runCeoPlanner } from './ceoPlanner';
import { runCeoReporter } from './ceoReporter';
import { buildSpecialistPrompt } from './promptBuilder';
import {
    appendAgentMemory,
    appendDecision,
    createSessionDir,
    newSessionTimestamp,
    readAgentMemory,
    readDecisions,
    writeAgentOutput,
    writeBrief,
    writeReport,
    writeSessionJson,
} from './sessionStore';
import { AgentTurnOutput, CompanyTaskPlan, SessionResult } from './types';

/** Trim length applied when an agent's output is fed into the next agent. */
const PEER_OUTPUT_BUDGET = 1500;

/**
 * Events emitted during a turn. The sidebar webview subscribes to render
 * progress (chips, headers, streamed agent replies). The shape is generic so
 * the same channel can carry CEO/agent/report messages without per-type
 * postMessage plumbing.
 */
export type CompanyTurnEvent =
    | { phase: 'plan-start' }
    | { phase: 'plan-ready'; plan: CompanyTaskPlan; parsed: boolean; raw: string }
    | { phase: 'agent-start'; agentId: string; task: string; index: number; total: number }
    | { phase: 'agent-done'; agentId: string; output: AgentTurnOutput; index: number; total: number }
    | { phase: 'report-start' }
    | { phase: 'report-done'; report: string; ok: boolean }
    | { phase: 'session-saved'; sessionDir: string }
    | { phase: 'aborted'; reason: string };

export type CompanyTurnEmitter = (event: CompanyTurnEvent) => void;

export interface DispatcherDeps {
    context: vscode.ExtensionContext;
    ai: IAIService;
    /** Default model to fall back to when an agent has no override. */
    defaultModel: string;
    /** Per-call cancellation. The sidebar's Stop button flips this. */
    signal?: AbortSignal;
    /** Optional event sink for the webview. Receives events synchronously. */
    onEvent?: CompanyTurnEmitter;
}

/**
 * Run a single company turn. Returns a fully-populated `SessionResult` even
 * on partial failure (so callers can always render *something* in chat).
 */
export async function runCompanyTurn(
    userPrompt: string,
    deps: DispatcherDeps,
): Promise<SessionResult> {
    const startedAt = Date.now();
    const state = readCompanyState(deps.context);
    const timestamp = newSessionTimestamp();
    const sessionDir = createSessionDir(deps.context, timestamp);

    const emit: CompanyTurnEmitter = deps.onEvent ?? (() => { /* noop */ });
    const isAborted = () => deps.signal?.aborted === true;
    const fail = (reason: string): SessionResult => {
        emit({ phase: 'aborted', reason });
        return {
            timestamp, sessionDir,
            userPrompt,
            plan: { brief: '', tasks: [] },
            agentOutputs: [],
            report: '',
            totalDurationMs: Date.now() - startedAt,
        };
    };
    if (isAborted()) return fail('signal-aborted');

    // ── Phase 1: planner ──
    emit({ phase: 'plan-start' });
    const ceoModel = modelForAgent(state, 'ceo', deps.defaultModel);
    const plannerResult = await runCeoPlanner(deps.ai, userPrompt, state, { model: ceoModel });
    if (isAborted()) return fail('aborted-after-plan');
    emit({
        phase: 'plan-ready',
        plan: plannerResult.plan,
        parsed: plannerResult.parsed,
        raw: plannerResult.raw,
    });
    writeBrief(sessionDir, userPrompt, plannerResult.plan);

    // ── Phase 2: sequential dispatch ──
    const outputs: AgentTurnOutput[] = [];
    const total = plannerResult.plan.tasks.length;
    for (let i = 0; i < total; i++) {
        if (isAborted()) return fail('aborted-mid-dispatch');
        const task = plannerResult.plan.tasks[i];
        emit({ phase: 'agent-start', agentId: task.agent, task: task.task, index: i, total });
        const turn = await _dispatchOne(task.agent, task.task, outputs, state, deps);
        outputs.push(turn);
        writeAgentOutput(sessionDir, turn);
        // Best-effort: append a one-line memory entry so the agent "remembers"
        // having done this task. Verbose successes are summarized in the CEO
        // report — memory is just the breadcrumb trail.
        appendAgentMemory(
            deps.context,
            task.agent,
            `[${timestamp}] ${task.task} — ${turn.error ? `❌ ${turn.error}` : '✅'}`,
        );
        emit({ phase: 'agent-done', agentId: task.agent, output: turn, index: i, total });
    }

    // ── Phase 3: synthesis ──
    if (isAborted()) return fail('aborted-before-report');
    emit({ phase: 'report-start' });
    const reportModel = modelForAgent(state, 'ceo', deps.defaultModel);
    const reportResult = await runCeoReporter(
        deps.ai,
        plannerResult.plan,
        outputs,
        state,
        { model: reportModel },
    );
    writeReport(sessionDir, reportResult.report);
    emit({ phase: 'report-done', report: reportResult.report, ok: reportResult.ok });

    // ── Phase 4: persist + side effects ──
    const result: SessionResult = {
        timestamp, sessionDir,
        userPrompt,
        plan: plannerResult.plan,
        agentOutputs: outputs,
        report: reportResult.report,
        totalDurationMs: Date.now() - startedAt,
    };
    writeSessionJson(sessionDir, result);
    // Heuristic: if the report mentions a 🚀 line, extract it as a decision.
    const decisionLine = reportResult.report.split(/\n/).find((l) => /^\d+\.\s+/.test(l.trim()));
    if (decisionLine) appendDecision(deps.context, decisionLine.trim());
    emit({ phase: 'session-saved', sessionDir });

    logInfo('company.dispatcher: turn complete.', {
        sessionDir, agents: outputs.length, ok: reportResult.ok,
        durationMs: result.totalDurationMs,
    });
    return result;
}

/**
 * Dispatch one specialist. Wraps the AI call with try/catch so a single
 * agent's failure never aborts the whole turn — we record the error and
 * keep going so the user still gets the other agents' outputs.
 */
async function _dispatchOne(
    agentId: string,
    task: string,
    earlierOutputs: AgentTurnOutput[],
    state: ReturnType<typeof readCompanyState>,
    deps: DispatcherDeps,
): Promise<AgentTurnOutput> {
    const startedAt = Date.now();
    const def = getCompanyAgent(agentId);
    if (!def) {
        return {
            agentId, task, response: '', durationMs: 0,
            error: `Unknown agent id: ${agentId}`,
        };
    }
    const memory = readAgentMemory(deps.context, agentId);
    const decisions = readDecisions(deps.context, 2000);
    const peerOutputs = earlierOutputs
        .filter((o) => !o.error) // skip failed peers — they'd just confuse the next agent
        .map((o) => {
            const peerDef = getCompanyAgent(o.agentId);
            const body = o.response.length > PEER_OUTPUT_BUDGET
                ? o.response.slice(0, PEER_OUTPUT_BUDGET) + '\n…(truncated)'
                : o.response;
            return {
                agentId: o.agentId,
                agentName: peerDef?.name ?? o.agentId,
                emoji: peerDef?.emoji ?? '🤖',
                content: body,
            };
        });

    const system = buildSpecialistPrompt({
        agentId, state,
        agentMemory: memory, sharedDecisions: decisions,
        peerOutputs,
    });
    const model = modelForAgent(state, agentId, deps.defaultModel);

    try {
        const result = await deps.ai.chat({
            system,
            user: task,
            model,
        });
        const response = (result.content || '').trim();
        return {
            agentId, task,
            response: response || '_(empty response)_',
            durationMs: Date.now() - startedAt,
            error: response ? undefined : 'empty-response',
        };
    } catch (e: any) {
        const err = e?.message ?? String(e);
        logError('company.dispatcher: agent dispatch failed.', { agentId, err });
        return {
            agentId, task,
            response: `⚠️ 호출 실패: ${err}`,
            durationMs: Date.now() - startedAt,
            error: err,
        };
    }
}