Multi-Agent Orchestration Expert (2026 Edition)

English | Bahasa Indonesia

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English

Orchestration & Integration

• Swarm Directorship & Planning: brainstorming, zero-to-prod-orchestrator, speculative-multi-draft-synthesizer
• Foundation Models & Providers: ai-llm-integration-expert, frontier-ai-models-expert, gemini-agent-booster, vercel-ai-sdk-expert
• Tool Execution & MCP Protocols: mcp-server-architect, parallel-tool-orchestrator, doku-mcp-server
• Agent Languages & Frameworks: pydantic-ai-expert, structured-output-contracts, python-programming-expert
• Memory, State & Telemetry: session-memory-manager, agentic-memory-architect, data-telemetry-expert
• Security & Quality Gates: anti-slop, mcp-sampling-critic, autonomous-red-teamer, zero-trust-secret-vault

Description

Expert guide for designing, building, and deploying production-grade multi-agent AI systems. Covers core agentic design patterns (Prompt Chaining, Routing, Parallelization, Orchestrator-Workers, Evaluator-Optimizer), stateful graph engines (LangGraph, OpenAI Agents SDK, Google ADK, Mastra.ai), shared episodic/semantic memory, tool execution sandboxes, and human-in-the-loop (HITL) guardrails.

Swarm Synergy: This skill acts as a master orchestrator when combined with mcp-server-architect (for external tool integration) and ai-llm-integration-expert (for foundation model setup). Together, they form a complete, end-to-end AI Engineering Swarm.

Trigger Conditions

• Building autonomous AI agents that execute complex, multi-step tasks across several domains.
• Designing systems where multiple specialized AI agents collaborate, deliberate, and cross-validate.
• Implementing stateful, graph-based agent workflows with LangGraph, OpenAI Agents SDK, or Google ADK.
• Implementing Anthropic agentic design patterns: Evaluator-Optimizer loops, Orchestrator-Workers, or Routing.
• Integrating human-in-the-loop (HITL) pause checkpoints for high-risk actions (code execution, database migrations, financial transactions).
• Evaluating and selecting agent architectures across Python, TypeScript, and multi-platform swarms.

Anthropic 2026 Core Agentic Design Patterns

Production systems should favor explicit Workflows over unbounded autonomous loops where predictability and reliability are required:

1. PROMPT CHAINING
   [Input] ---> [LLM Step 1] ---> [Gate/Validator] ---> [LLM Step 2] ---> [Output]

2. ROUTING
   [Input] ---> [Classifier/Router] ──┬──> [Specialist Agent A]
                                     ├──> [Specialist Agent B]
                                     └──> [Specialist Agent C]

3. PARALLELIZATION (Sectioning & Voting)
   [Input] ──┬──> [Task 1 (Subagent)] ──┐
             ├──> [Task 2 (Subagent)] ──┼──> [Aggregator / Synthesizer]
             └──> [Task 3 (Subagent)] ──┘

4. ORCHESTRATOR-WORKERS (Dynamic Decomposition)
   [Input] ---> [Orchestrator] ──┬──> [Worker 1 (Focused Context)] ──┐
                                 ├──> [Worker 2 (Focused Context)] ──┼──> [Orchestrator Synthesis]
                                 └──> [Worker 3 (Focused Context)] ──┘

5. EVALUATOR-OPTIMIZER LOOP (Zero-Tolerance Quality Gate)
   [Input] ---> [Generator Agent] <─────┐ (Feedback Loop)
                       │                 │
                       ▼                 │
               [Evaluator / Auditor] ────┘ (Reject / Needs Revision)
                       │
                       ▼ (Approved)
                   [Output]

Bridging Internal Swarm Patterns

vibes-plug's internal Swarm Director patterns (from AGENTS.md) map directly to these external frameworks:

• Fan-Out / Fan-In topology: Mapped via LangGraph parallel node execution + reducer functions, or Google ADK sub-agent arrays.
• Pipeline Saga topology: Mapped via OpenAI Agents SDK sequential handoffs or Mastra.ai sequential chains.
• Critic-Validator Loop topology: Mapped via LangGraph conditional edges routing back to generator nodes.

Agent Framework Comparison (2026)

Framework	Language	Best For	Key Differentiator
LangGraph (v0.3+)	Python / TypeScript	Complex stateful workflows & graphs	Graph-based, persistent checkpointers, time-travel debugging
OpenAI Agents SDK	Python	GPT-4.5 / o4-series native agents	Built-in agent handoffs, tracing, and tripwire guardrails
Google ADK	Python	Gemini-powered swarms	Native Vertex AI, multi-agent streaming, search grounding
Mastra.ai	TypeScript	TS-first web apps & microservices	Built-in memory, evals, RAG, and native MCP support
CrewAI	Python	Role-playing business teams & flows	Event-driven Flows, hierarchical manager execution, multi-tier memory

Core Implementation Guidelines

1. LangGraph — Persistent State & HITL Checkpoints

LangGraph models agent workflows as directed acyclic or cyclic graphs with persistent state:

from langgraph.graph import StateGraph, END
from langgraph.checkpoint.memory import MemorySaver
from typing import TypedDict, Annotated
import operator

class AgentState(TypedDict):
    messages: Annotated[list, operator.add]
    task: str
    code_artifact: str
    audit_feedback: str
    approved: bool

def generator_node(state: AgentState):
    # Generates or refactors code based on previous feedback
    code = coder_agent.invoke(state["task"], feedback=state.get("audit_feedback"))
    return {"code_artifact": code}

def evaluator_node(state: AgentState):
    # Runs automated linter/tests & security review
    audit = auditor_agent.invoke(state["code_artifact"])
    return {
        "audit_feedback": audit.critique,
        "approved": audit.is_passing
    }

def route_next(state: AgentState) -> str:
    return END if state["approved"] else "generator"

builder = StateGraph(AgentState)
builder.add_node("generator", generator_node)
builder.add_node("evaluator", evaluator_node)
builder.set_entry_point("generator")
builder.add_edge("generator", "evaluator")
builder.add_conditional_edges("evaluator", route_next, {"generator": "generator", END: END})

# Persist state with checkpointer for HITL interruption before destructive actions
checkpointer = MemorySaver()
graph = builder.compile(checkpointer=checkpointer, interrupt_before=["generator"])

TypeScript LangGraph Implementation:

import { StateGraph, MemorySaver, END } from "@langchain/langgraph";

const graphState = {
  messages: { value: (x, y) => x.concat(y), default: () => [] },
  approved: { value: (x, y) => y, default: () => false }
};

const builder = new StateGraph({ channels: graphState })
  .addNode("generator", async (state) => ({ messages: [await coder.invoke(state)] }))
  .addNode("evaluator", async (state) => {
    const res = await auditor.invoke(state);
    return { messages: [res.critique], approved: res.isPassing };
  })
  .addEdge("__start__", "generator")
  .addEdge("generator", "evaluator")
  .addConditionalEdges("evaluator", (state) => state.approved ? END : "generator");

const checkpointer = new MemorySaver();
const graph = builder.compile({ checkpointer, interruptBefore: ["generator"] });

2. OpenAI Agents SDK — Agent Handoffs & Guardrails

Implement native agent handoffs where specialized agents transition control cleanly:

from agents import Agent, Runner, handoff, input_guardrail, GuardrailFunctionOutput

researcher = Agent(
    name="Researcher",
    instructions="Research libraries, security advisories, and system specs.",
    tools=[web_search, doc_retrieval],
)

architect = Agent(
    name="Architect",
    instructions="Synthesize technical architecture and delegate research when needed.",
    handoffs=[handoff(researcher, tool_name_override="delegate_research")],
)

@input_guardrail
async def safety_guardrail(ctx, agent, input_data) -> GuardrailFunctionOutput:
    if contains_destructive_commands(input_data):
        return GuardrailFunctionOutput(output_info="Blocked destructive payload", tripwire_triggered=True)
    return GuardrailFunctionOutput(output_info="Safe", tripwire_triggered=False)

result = await Runner.run(architect, "Design high-throughput ingestion pipeline", guardrails=[safety_guardrail])

3. Google ADK — Gemini Multi-Agent Systems

Orchestrate Gemini 3.x agents with streaming subagent calls and Vertex AI tooling:

from google.adk.agents import Agent
from google.adk.tools import google_search, code_execution

director = Agent(
    model="gemini-3.1-pro",
    name="director",
    instruction="Coordinate domain specialists and synthesize final deliverables.",
    sub_agents=[frontend_agent, backend_agent, security_agent],
    tools=[google_search, code_execution],
)

4. Mastra.ai — TypeScript-Native Agents

For modern Next.js / Node.js / Bun environments:

import { Agent, MastraMemory } from '@mastra/core';
import { createTool } from '@mastra/core/tools';
import { z } from 'zod';

const researcher = new Agent({
  name: 'researcher',
  instructions: 'Find and summarize accurate technical documentation.',
  model: { provider: 'ANTHROPIC', name: 'claude-3-7-sonnet-20250219' },
  memory: new MastraMemory({ storage: supabaseStorage }),
});

5. CrewAI — Role-Playing Crews, Hierarchical Execution & Event-Driven Flows

Combines event-driven application state flows (Flow) with autonomous role-playing agent crews (Crew), supporting hierarchical delegation and typed Pydantic outputs:

from crewai import Agent, Crew, Process, Task
from crewai.tools import tool
from crewai.flow.flow import Flow, start, listen, router
from pydantic import BaseModel, Field

# 1. Typed State and Output Schemas
class SecurityReport(BaseModel):
    is_vulnerable: bool
    vulnerabilities: list[str] = Field(default_factory=list)
    mitigation_plan: str

class PipelineState(BaseModel):
    target_service: str = "payment-gateway"
    audit_passed: bool = False
    report: SecurityReport | None = None

# 2. Custom Safe Tool
@tool("scan_dependencies")
def scan_dependencies(service_name: str) -> str:
    """Scans service dependencies for known CVEs and outdated packages."""
    return f"Scan complete for {service_name}: 0 critical, 1 low severity vulnerability in OpenSSL binding."

# 3. Define Event-Driven Flow with State & Conditional Branching
class DeploymentFlow(Flow[PipelineState]):
    @start()
    def initialize_pipeline(self):
        print(f"Initializing security pipeline for: {self.state.target_service}")
        return self.state.target_service

    @listen(initialize_pipeline)
    def run_security_crew(self, service: str):
        # Define Specialized Role-Playing Agents
        scanner = Agent(
            role="Security Researcher",
            goal=f"Audit dependencies and network vectors for {service}",
            backstory="Veteran cybersecurity auditor specialized in FinTech compliance and OWASP Top 10.",
            tools=[scan_dependencies],
            verbose=True,
            memory=True
        )

        hardener = Agent(
            role="Systems Hardener",
            goal="Formulate actionable mitigation plans for all identified security vectors",
            backstory="Principal DevSecOps engineer with deep zero-trust and kernel security expertise.",
            verbose=True,
            memory=True
        )

        # Define Tasks with Structured Output and HITL Checkpoint
        audit_task = Task(
            description=f"Analyze all package dependencies and attack surfaces for {service}.",
            expected_output="Raw vulnerability scan assessment.",
            agent=scanner
        )

        remediation_task = Task(
            description="Produce a structured mitigation report and verify if the service is safe for deploy.",
            expected_output="A typed JSON security report matching the SecurityReport schema.",
            agent=hardener,
            output_pydantic=SecurityReport,
            human_input=True
        )

        # Instantiate Hierarchical Crew with Manager LLM
        security_crew = Crew(
            agents=[scanner, hardener],
            tasks=[audit_task, remediation_task],
            process=Process.hierarchical,
            manager_llm="gpt-4o",
            memory=True,
            verbose=True
        )

        result = security_crew.kickoff()
        self.state.report = result.pydantic
        self.state.audit_passed = not self.state.report.is_vulnerable
        return self.state.audit_passed

    @router(run_security_crew)
    def evaluate_deployment(self):
        if self.state.audit_passed:
            return "deploy"
        return "quarantine"

    @listen("deploy")
    def handle_deployment(self):
        print(f"Service {self.state.target_service} approved for zero-downtime deployment.")

    @listen("quarantine")
    def handle_quarantine(self):
        print(f"Deployment halted: {self.state.target_service} quarantined for remediation.")

6. Human-in-the-Loop (HITL) Guardrails

Mandatory safeguards before executing irreversible operations:

• Interrupt Checkpoints: Halt workflow execution before executing code, migrating databases, or modifying production records.
• Approval Dashboards: Surface diff previews and proposed shell commands to the user or admin before proceeding.
• Confidence Gates: Auto-proceed only when model confidence score is >= 0.90; trigger human escalation otherwise.

7. Swarm Circuit Breakers & Fallback Protocols

• Retry Caps: Maximum 2 automated retries per subagent.
• Fallback Escalation: If a specialist agent stalls or loops, the Swarm Director gracefully fallbacks to fullstack-expert or requests human guidance.
• Checkpoint Persistence: Always persist intermediate progress to PROGRESS.md or BLUEPRINT.md so sessions can resume without losing context.

8. Narrative Simulation Swarms (Fable Paradigm)

Multi-agent autonomous story world simulation architecture where agents act as characters.

• Character-Agent Personality Encoding: Uses Big Five personality model + emotional valence vectors (joy, anger, fear, surprise, sadness, disgust).
• Inter-Agent Dialogue Protocols: Constrained by narrative coherence.
• World-State Consensus Protocol: Distributed shared memory with conflict resolution to maintain a consistent simulated reality.
• Autonomous Episodic Generation: Agents create story episodes dynamically without human prompting.
• Director Agent Pattern: A meta-agent that monitors the swarm and ensures narrative arc consistency.

import { StateGraph, END } from "@langchain/langgraph";
import { BaseMessage, SystemMessage } from "@langchain/core/messages";

interface WorldState {
  messages: BaseMessage[];
  events: string[];
}

const romeoAgent = async (state: WorldState) => {
  // Encoded with High Openness, High Neuroticism, emotional vectors
  const response = await llm.invoke([
    new SystemMessage("You are Romeo. You are feeling [Joy: 0.8, Sadness: 0.2]. Respond to the world state."),
    ...state.messages
  ]);
  return { messages: [response] };
};

const directorAgent = async (state: WorldState) => {
  // Ensures narrative arc consistency
  const evaluation = await evaluatorLLM.invoke(state.messages);
  return { events: [evaluation.content] };
};

9. Computer-Using Agent (CUA) Orchestration

• CUA Agent Delegation: Swarm director delegates specific UI tasks to CUA worker agents.
• Screen-Sharing Observation: Orchestrator agent observes CUA's visual stream to verify progress.
• Recovery Protocols: Handles CUA failures like stuck UI states or navigation errors via visual feedback loops.
• Parallel CUA Execution: Multiple CUA workers operate different browser tabs/windows simultaneously.

import { CUARunner, CUAWorker } from "cua-orchestration-sdk";

const orchestrator = new CUARunner();
const worker1 = new CUAWorker({ id: "tab-1", objective: "Scrape pricing page" });
const worker2 = new CUAWorker({ id: "tab-2", objective: "Monitor system health" });

orchestrator.registerWorkers([worker1, worker2]);
orchestrator.on("worker_stuck", async (worker, screenshot) => {
  await orchestrator.recoverWorker(worker, screenshot);
});
await orchestrator.executeParallel();

10. Continuous Perception Swarms

• Always-on Monitoring: 24/7 perception loops capturing multimodal input.
• Live Video/Audio Triage Agents: (Intake → Classify → Route) pipelines processing continuous streams.
• Spatial Awareness Distribution: Sharing spatial context across the agent swarm.
• Event-Driven Wakeup Protocols: Agents remain dormant until a relevant stimulus is detected.
• Gemini Multimodal Live API Integration: Native hooks for continuous audio/video perception.

import { MultimodalLiveClient } from "gemini-live-sdk";
import { TriageSwarm } from "./swarm";

const client = new MultimodalLiveClient({ apiKey: process.env.GEMINI_API_KEY });
const swarm = new TriageSwarm();

client.on("video_frame", async (frame) => {
  const classification = await swarm.intake(frame);
  if (classification.isCritical) {
    swarm.wakeupSpecialists(classification.type);
    await swarm.route(frame, classification.type);
  }
});
client.connect();

11. Massive Parallel Swarms (Agentic MoE) for Next-Gen LLMs

For next-gen models like Gemini 4 Pro:

• Batched Tool Invocation: Transition from sequential step-by-step orchestrators to massive batched function calling.
• Monolithic Context Flow: Pass the entire massive context (codebase snapshot) directly via KV-Cache rather than using chunked RAG retrieval per agent, allowing subagents to natively attend to the exact same shared memory state instantly.

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Bahasa Indonesia

Integrasi Orkestrasi

• Direktur Swarm & Perencanaan: brainstorming, zero-to-prod-orchestrator, speculative-multi-draft-synthesizer
• Model Fondasi & Penyedia: ai-llm-integration-expert, frontier-ai-models-expert, gemini-agent-booster, vercel-ai-sdk-expert
• Eksekusi Alat & Protokol MCP: mcp-server-architect, parallel-tool-orchestrator, doku-mcp-server
• Bahasa & Framework Agen: pydantic-ai-expert, structured-output-contracts, python-programming-expert
• Memori, State & Telemetri: session-memory-manager, agentic-memory-architect, data-telemetry-expert
• Keamanan & Gerbang Kualitas: anti-slop, mcp-sampling-critic, autonomous-red-teamer, zero-trust-secret-vault

Deskripsi

Panduan ahli untuk merancang, membangun, dan men-deploy sistem multi-agen AI tingkat produksi. Mencakup pola desain agentik inti (Prompt Chaining, Routing, Parallelization, Orchestrator-Workers, Evaluator-Optimizer), engine graph stateful (LangGraph, OpenAI Agents SDK, Google ADK, Mastra.ai), memori bersama episodik/semantik, sandbox eksekusi tool, dan guardrail human-in-the-loop (HITL).

Sinergi Swarm: Skill ini bertindak sebagai orkestrator utama jika dipadukan dengan mcp-server-architect (untuk integrasi tool eksternal) dan ai-llm-integration-expert (untuk konfigurasi foundation model). Bersama-sama, ketiganya membentuk AI Engineering Swarm yang tangguh dari awal hingga rilis produksi.

Kondisi Pemicu

• Membangun agen AI otonom yang mengeksekusi tugas kompleks multi-langkah lintas domain.
• Merancang sistem kolaborasi, deliberasi, dan validasi silang antar beberapa agen AI spesialis.
• Mengimplementasikan alur kerja graph stateful dengan LangGraph, OpenAI Agents SDK, atau Google ADK.
• Menerapkan 5 pola desain agentik standar: Prompt Chaining, Routing, Parallelization, Orchestrator-Workers, atau Evaluator-Optimizer.
• Mengintegrasikan pos henti human-in-the-loop (HITL) untuk tindakan berisiko tinggi (eksekusi kode, migrasi database, transaksi keuangan).
• Memilih dan mengevaluasi arsitektur agen di ekosistem Python, TypeScript, atau multi-platform.

5 Pola Desain Agentik Inti (Standar Anthropic 2026)

Untuk sistem produksi yang handal, utamakan arsitektur Workflows terstruktur daripada loop otonom tanpa batas:

1. Prompt Chaining: Memecah tugas menjadi langkah-langkah sekuensial dengan validasi output di setiap transisi.
2. Routing: Mengklasifikasikan input pengguna dan mengarahkannya ke model atau sub-agen yang memiliki spesialisasi yang tepat.
3. Parallelization (Sectioning & Voting): Menjalankan beberapa sub-agen secara simultan untuk tugas independen atau menjalankan ensemble untuk konsensus voting.
4. Orchestrator-Workers: Agen orkestrator pusat memecah masalah dinamis, mendelegasikannya ke pekerja dengan konteks terfokus, lalu merangkum hasil akhirnya.
5. Evaluator-Optimizer Loop: Agen pembuat (generator) menghasilkan solusi sementara agen penilai (evaluator) memberikan audit dan umpan balik hingga standar kualitas terpenuhi.

Perbandingan Framework Agen (2026)

Framework	Bahasa	Terbaik Untuk	Keunggulan Utama
LangGraph (v0.3+)	Python / TypeScript	Alur kerja graf stateful kompleks	Berbasis graf, checkpointer persisten, time-travel debugging
OpenAI Agents SDK	Python	Agen native GPT-4.5 / o4-series	Handoff antar agen bawaan, tracing, dan guardrail otomatis
Google ADK	Python	Swarm agen bertenaga Gemini	Integrasi Vertex AI native, streaming multi-agen, search grounding
Mastra.ai	TypeScript	Web apps & microservice TS-first	Memori bawaan, evaluasi otomatis, RAG, dan dukungan MCP native
CrewAI	Python	Tim kolaborasi peran & flow terstruktur	Event-driven Flows, eksekusi manajer hierarkis, memori multi-tier

Panduan Implementasi Inti

1. LangGraph — State Persisten & Checkpoint HITL

Memodelkan alur agen sebagai graf terarah dengan state bersama dan penyimpanan checkpoint:

• Simpan state di database (PostgreSQL / MemorySaver) agar alur kerja dapat dijeda dan dilanjutkan kapan saja.
• Terapkan interrupt_before sebelum node yang menjalankan perintah destruktif untuk meminta persetujuan manusia (Human-in-the-loop).

2. OpenAI Agents SDK — Handoffs & Guardrails

Terapkan transisi kendali yang mulus antar agen dengan fungsi handoff bawaan serta pasang filter guardrail pada input dan output untuk mencegah eksekusi instruksi berbahaya.

3. Google ADK — Multi-Agent Gemini

Bangun hierarki agen dengan model Gemini 3.x, di mana root agent mengoordinasikan sub-agents untuk riset, eksekusi kode, dan pembuatan dokumen.

4. Mastra.ai — Solusi TypeScript Penuh

Gunakan Mastra untuk ekosistem Next.js dan Node.js: sediakan memori persisten ke Supabase/PostgreSQL, integrasikan tool MCP secara langsung, dan manfaatkan framework evaluasi bawaan.

5. CrewAI — Tim Kolaborasi Agen, Eksekusi Hierarkis & Event-Driven Flows

Menggabungkan alur status aplikasi berbasis peristiwa (Flow) dengan tim agen otonom berbasis peran (Crew), mendukung delegasi hierarkis dan output Pydantic yang terstruktur:

from crewai import Agent, Crew, Process, Task
from crewai.tools import tool
from crewai.flow.flow import Flow, start, listen, router
from pydantic import BaseModel, Field

# 1. Skema State dan Output Terstruktur
class LaporanKeamanan(BaseModel):
    is_vulnerable: bool
    vulnerabilities: list[str] = Field(default_factory=list)
    mitigation_plan: str

class StatePipeline(BaseModel):
    target_service: str = "payment-gateway"
    audit_passed: bool = False
    laporan: LaporanKeamanan | None = None

# 2. Tool Kustom Aman
@tool("pindai_dependensi")
def pindai_dependensi(service_name: str) -> str:
    """Memindai dependensi layanan terhadap kerentanan CVE yang diketahui."""
    return f"Pemindaian {service_name}: 0 kerentanan kritis, 1 peringatan tingkat rendah pada modul OpenSSL."

# 3. Alur Kerja Event-Driven (Flow) dengan State & Percabangan Kondisional
class PipelineDeploymentFlow(Flow[StatePipeline]):
    @start()
    def inisialisasi_pipeline(self):
        print(f"Menginisialisasi pipeline audit keamanan untuk: {self.state.target_service}")
        return self.state.target_service

    @listen(inisialisasi_pipeline)
    def jalankan_crew_keamanan(self, service: str):
        # Definisikan Agen Kolaboratif Berbasis Peran
        auditor = Agent(
            role="Auditor Keamanan",
            goal=f"Audit dependensi dan celah serangan untuk {service}",
            backstory="Pakar keamanan siber spesialisasi regulasi FinTech dan standar OWASP Top 10.",
            tools=[pindai_dependensi],
            verbose=True,
            memory=True
        )

        engineer = Agent(
            role="DevSecOps Engineer",
            goal="Menyusun langkah mitigasi terstruktur untuk seluruh temuan keamanan",
            backstory="Arsitek infrastruktur zero-trust dan rekayasa keandalan sistem.",
            verbose=True,
            memory=True
        )

        # Definisikan Tugas dengan Output Terstruktur dan Checkpoint HITL
        tugas_audit = Task(
            description=f"Periksa seluruh dependensi pustaka dan attack surface pada {service}.",
            expected_output="Hasil pemindaian dependensi mentah.",
            agent=auditor
        )

        tugas_mitigasi = Task(
            description="Buat laporan mitigasi dan verifikasi apakah layanan aman untuk di-deploy.",
            expected_output="Laporan JSON terstruktur sesuai skema LaporanKeamanan.",
            agent=engineer,
            output_pydantic=LaporanKeamanan,
            human_input=True
        )

        # Inisialisasi Crew dengan Eksekusi Manajer Hierarkis
        crew_keamanan = Crew(
            agents=[auditor, engineer],
            tasks=[tugas_audit, tugas_mitigasi],
            process=Process.hierarchical,
            manager_llm="gpt-4o",
            memory=True,
            verbose=True
        )

        hasil = crew_keamanan.kickoff()
        self.state.laporan = hasil.pydantic
        self.state.audit_passed = not self.state.laporan.is_vulnerable
        return self.state.audit_passed

    @router(jalankan_crew_keamanan)
    def evaluasi_rilis(self):
        if self.state.audit_passed:
            return "deploy"
        return "karantina"

    @listen("deploy")
    def tangani_deploy(self):
        print(f"Layanan {self.state.target_service} disetujui untuk rilis produksi zero-downtime.")

    @listen("karantina")
    def tangani_karantina(self):
        print(f"Rilis ditangguhkan: {self.state.target_service} dikarantina untuk perbaikan celah keamanan.")

6. Guardrails Human-in-the-Loop (HITL)

Pengamanan wajib sebelum melakukan tindakan yang tidak dapat dibatalkan:

• Pos Henti Interupsi: Hentikan eksekusi sebelum menjalankan skrip shell berbahaya, migrasi skema tabel, atau memodifikasi data produksi.
• Tinjauan Pratinjau: Tampilkan ringkasan perbedaan (diff) kepada pengguna sebelum modifikasi dieksekusi.
• Ambang Keyakinan: Otomatis lanjutkan hanya jika skor keyakinan model >= 0.90; eskalasikan ke manusia jika berada di bawah ambang batas.

7. Circuit Breakers & Protokol Pemulihan Swarm

• Batas Percobaan Ulang: Maksimal 2 kali perbaikan otomatis per sub-agen.
• Eskalasi Fallback: Jika agen spesialis mengalami kendala konteks atau gagal berulang kali, Swarm Director segera mengalihkan tugas ke fullstack-expert atau meminta masukan pengguna.
• Persistensi Kemajuan: Simpan selalu checkpoint di PROGRESS.md atau BLUEPRINT.md agar alur kerja dapat dilanjutkan secara efisien tanpa token berlebih.

8. Swarm Simulasi Naratif (Paradigma Fable)

Arsitektur simulasi dunia cerita otonom multi-agen di mana agen bertindak sebagai karakter.

• Pengkodean Kepribadian Karakter-Agen: Menggunakan model kepribadian Big Five + vektor valensi emosional.
• Protokol Dialog Antar-Agen: Dibatasi oleh koherensi naratif.
• Protokol Konsensus Status Dunia: Memori bersama terdistribusi dengan penyelesaian konflik.
• Pola Agen Sutradara (Director): Meta-agen yang memastikan konsistensi alur cerita.

9. Orkestrasi Computer-Using Agent (CUA)

• Delegasi Agen CUA: Sutradara mendelegasikan tugas UI ke agen pekerja CUA.
• Observasi Berbagi Layar: Orkestrator memantau aliran visual CUA.
• Protokol Pemulihan: Menangani kegagalan CUA (UI macet) melalui loop umpan balik visual.
• Eksekusi CUA Paralel: Berbagai agen mengoperasikan tab browser berbeda secara bersamaan.

10. Swarm Persepsi Berkelanjutan

• Pemantauan Selalu Aktif: Loop persepsi 24/7 yang menangkap input multimodal (video/audio).
• Agen Triase Langsung: Pipeline (Intake → Klasifikasi → Rute).
• Protokol Bangun Berbasis Peristiwa (Event-Driven): Agen tidur hingga mendeteksi stimulus yang relevan.
• Integrasi API Live Multimodal Gemini: Hook bawaan untuk pemrosesan persepsi berkelanjutan.

11. Swarm Paralel Masif (Agentic MoE) untuk LLM Next-Gen

Untuk model generasi berikutnya seperti Gemini 4 Pro:

• Pemanggilan Tool Massal (Batching): Beralih dari orkestrator sekuensial (bertahap) ke pemanggilan fungsi massal secara serentak.
• Aliran Konteks Monolitik: Kirimkan seluruh konteks masif (snapshot codebase) secara langsung melalui KV-Cache, hindari RAG terfragmentasi per agen. Ini memungkinkan sub-agen menganalisis state memori yang sama secara instan.