ADK ve A2A ile çoklu aracı maraton planlayıcı oluşturma

1. Giriş

Bu codelab'de, üç uzmanlaşmış yapay zeka temsilcisinin bir şehir maratonunu planlamak için işbirliği yaptığı çoklu temsilci sistemi oluşturacaksınız. Bir Maraton Planlayıcı temsilcisi tasarımı düzenler, bir Değerlendirici temsilcisi Vertex AI Evaluation'ı kullanarak plan kalitesini puanlar ve bir Simülasyon Denetleyicisi temsilcisi hazırlığı doğrular. Tüm bu işlemler, Temsilciden Temsilciye (A2A) protokolü üzerinden iletişim kurularak gerçekleştirilir.

Yapacaklarınız

• Vertex AI'daki Gemini modellerini kullanarak Agent Development Kit (ADK) ile yapay zeka aracı oluşturma
• Güvenilir aracı yanıtları için Pydantic şemalarıyla yapılandırılmış çıkışlar tanımlama
• MetricPromptBuilder (LLM-as-Judge) kullanarak özel Vertex AI Evaluation metrikleri oluşturma
• AgentTool ile aracıları alt aracı olarak bağlayın ve uzak aracıları A2A protokolü üzerinden bağlayın.
• Prosedürel bilgi için ADK Skills'i, oturumlar arası öğrenme için Memory Bank'ı kullanın.
• A2A sunucularıyla yerel olarak ajan sunma ve çok ajanlı ortak çalışmayı test etme

İhtiyacınız olanlar

• Chrome gibi bir web tarayıcısı
• Faturalandırmanın etkin olduğu bir Google Cloud projesi

Bu codelab, Python ve yapay zeka kavramlarına aşina olan orta düzey geliştiriciler içindir.

Tahmini tamamlama süresi: 90 dakika.

Bu kod laboratuvarında oluşturulan kaynakların maliyeti 5 ABD dolarından az olmalıdır.

2. Başlamadan önce

Google Cloud projesi oluşturma

1. Google Cloud Console'da bir Google Cloud projesi seçin veya oluşturun.
2. Cloud projeniz için faturalandırmanın etkinleştirildiğinden emin olun.

Cloud Shell'i Başlatma

1. Google Cloud Console'un üst kısmından Cloud Shell'i etkinleştir'i tıklayın.
2. Kimlik doğrulamayı doğrulayın:

gcloud auth list

1. Projenizi onaylayın:

gcloud config get project

1. Gerekirse ayarlayın:

export PROJECT_ID=<YOUR_PROJECT_ID>
gcloud config set project $PROJECT_ID

API'leri etkinleştir

gcloud services enable aiplatform.googleapis.com

Python paket yöneticisini yükleme

Hızlı bir Python paket yöneticisi olan uv'yi yükleyin:

curl -LsSf https://astral.sh/uv/install.sh | sh
source $HOME/.local/bin/env

3. Projeyi oluşturma

Bu adımda proje yapısını oluşturur, bağımlılıkları yükler ve tüm aracıların kullandığı paylaşılan hizmetleri yapılandırırsınız.

Proje dizinini oluşturma

mkdir -p marathon-agents && cd marathon-agents

Proje yapılandırmasını oluşturma

Oluşturma pyproject.toml:

[project]
name = "marathon-agents"
version = "0.1.0"
description = "Multi-agent marathon planning system with ADK, A2A, and Vertex AI Evaluation"
readme = "README.md"
requires-python = ">=3.10"
dependencies = [
    "google-cloud-aiplatform[agent_engines,adk,evaluation]>=1.121.0",
    "google-adk>=1.25.0",
    "a2a-sdk>=0.3.9",
    "pydantic>=2.12.0",
    "python-dotenv>=1.0.0",
    "httpx>=0.27.0",
    "uvicorn>=0.30.0",
    "google-auth>=2.0.0",
    "pandas>=2.0.0",
]

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[tool.hatch.build.targets.wheel]
packages = ["src"]

[tool.pytest.ini_options]
asyncio_mode = "auto"
testpaths = ["tests"]

Bağımlıları yükleme

uv sync

Ortam değişkenlerini yapılandırma

.env dosyası oluşturma:

cat > .env << 'EOF'
GOOGLE_CLOUD_PROJECT=<YOUR_PROJECT_ID>
GOOGLE_CLOUD_LOCATION=us-central1
GOOGLE_GENAI_USE_VERTEXAI=true
EOF

Önemli: kısmını gerçek Google Cloud projesi kimliğinizle değiştirin.

Kaynak dizin ağacını oluşturma

Proje, ADK'nın çoklu aracı kuralına uyar. Her aracı, src/ altında bağımsız bir pakettir:

mkdir -p src/planner_agent/{agent,evaluator/{services},skills/{route-planning/references,plan-evaluation/references},services,runtime}
mkdir -p src/simulator_agent/{agent,skills/review-marathon-plan/references,services,runtime}

Python paketlerini başlatma

touch src/__init__.py
touch src/planner_agent/__init__.py src/planner_agent/agent/__init__.py
touch src/planner_agent/evaluator/__init__.py src/planner_agent/evaluator/services/__init__.py
touch src/planner_agent/services/__init__.py src/planner_agent/runtime/__init__.py
touch src/simulator_agent/__init__.py src/simulator_agent/agent/__init__.py
touch src/simulator_agent/services/__init__.py src/simulator_agent/runtime/__init__.py

Paylaşılan yapılandırmayı oluşturma

Oluşturma src/config.py — tüm aracılar için paylaşılan GCP yapılandırması:

"""Shared configuration for the multi-agent system."""

import os
from dotenv import load_dotenv

load_dotenv()

# GCP Configuration
GOOGLE_CLOUD_PROJECT = os.environ.get("GOOGLE_CLOUD_PROJECT")
GOOGLE_CLOUD_LOCATION = os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")
BUCKET_URI = os.environ.get("BUCKET_URI")


def validate_config():
    """Validate required configuration is set."""
    required = {
        "GOOGLE_CLOUD_PROJECT": GOOGLE_CLOUD_PROJECT,
    }
    missing = [k for k, v in required.items() if not v]
    if missing:
        raise ValueError(
            f"Missing required environment variables: {', '.join(missing)}. "
            "Check your .env file."
        )
    return True

Bellek Yöneticisi'ni oluşturma

Her temsilci, oturumlar arası öğrenme için Bellek Bankası kullanır. Oluşturma src/planner_agent/services/memory_manager.py:

"""Memory Manager for Marathon Planner Agent.

Manages Memory Bank integration with custom topics.
Enables cross-session learning.
"""

import os
from typing import TYPE_CHECKING

from google.adk.memory import VertexAiMemoryBankService
from vertexai._genai.types import (
    MemoryBankCustomizationConfig,
    MemoryBankCustomizationConfigMemoryTopic as MemoryTopic,
    MemoryBankCustomizationConfigMemoryTopicCustomMemoryTopic as CustomMemoryTopic,
)

if TYPE_CHECKING:
    from google.adk.agents.callback_context import CallbackContext


# Custom memory topics for cross-session learning
PLANNING_HISTORY = MemoryTopic(
    custom_memory_topic=CustomMemoryTopic(
        label="planning_history",
        description="""Track marathon plans generated across sessions.
        Extract: City, date, key route decisions, final score, iteration count.
        Format: "Plan: city={city}, date={date}, score={score}, iterations={n}"
        """,
    )
)

USER_PREFERENCES = MemoryTopic(
    custom_memory_topic=CustomMemoryTopic(
        label="user_preferences",
        description="""Track user preferences and constraints across sessions.
        Extract: Preferred themes, budget priorities, scale, city preferences.
        Format: "Preference: type={type}, value={value}"
        """,
    )
)

ROUTE_PATTERNS = MemoryTopic(
    custom_memory_topic=CustomMemoryTopic(
        label="route_patterns",
        description="""Track successful route patterns and lessons learned.
        Extract: Route segments with positive feedback, landmark combinations.
        Format: "Route: city={city}, pattern={pattern}, outcome={outcome}"
        """,
    )
)

LOGISTICS_INSIGHTS = MemoryTopic(
    custom_memory_topic=CustomMemoryTopic(
        label="logistics_insights",
        description="""Track logistics decisions and capacity learnings.
        Extract: Water station placement, venue capacity, crowd management.
        Format: "Logistics: category={cat}, insight={insight}"
        """,
    )
)


def create_marathon_planner_memory_topics() -> MemoryBankCustomizationConfig:
    """Create Memory Bank customization config with custom topics."""
    return MemoryBankCustomizationConfig(
        memory_topics=[
            PLANNING_HISTORY, USER_PREFERENCES,
            ROUTE_PATTERNS, LOGISTICS_INSIGHTS,
        ]
    )


def create_memory_service(
    project: str | None = None,
    location: str | None = None,
    agent_engine_id: str | None = None,
) -> VertexAiMemoryBankService | None:
    """Create a VertexAiMemoryBankService.

    Returns None if agent_engine_id is not set (local development).
    """
    project = project or os.environ.get("GOOGLE_CLOUD_PROJECT")
    location = location or (
        os.environ.get("AGENT_ENGINE_LOCATION")
        or os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")
    )
    agent_engine_id = agent_engine_id or os.environ.get("AGENT_ENGINE_ID")

    if not project:
        raise ValueError("GOOGLE_CLOUD_PROJECT environment variable required")
    if not agent_engine_id:
        return None

    return VertexAiMemoryBankService(
        project=project, location=location,
        agent_engine_id=agent_engine_id,
    )


async def auto_save_memories(callback_context: "CallbackContext") -> None:
    """Automatically save session to Memory Bank after agent responds."""
    project_id = os.environ.get("GOOGLE_CLOUD_PROJECT")
    location = (
        os.environ.get("AGENT_ENGINE_LOCATION")
        or os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")
    )
    agent_engine_id = os.environ.get("AGENT_ENGINE_ID")

    if not agent_engine_id:
        return

    try:
        memory_service = VertexAiMemoryBankService(
            project=project_id, location=location,
            agent_engine_id=agent_engine_id,
        )
        await memory_service.add_session_to_memory(
            callback_context._invocation_context.session
        )
    except Exception as e:
        print(f"Warning: Failed to save memories: {e}")

auto_save_memories geri çağırma işlevi, her temsilci yanıtından sonra çalışır ve Temsilci Motoru'na dağıtıldığında görüşmeyi Bellek Bankası'nda kalıcı hale getirir. Yerel geliştirme sırasında AGENT_ENGINE_ID ayarlanmamışsa bu adım sorunsuz bir şekilde atlanır.

Oturum Yöneticisi'ni oluşturma

Oluşturma src/planner_agent/services/session_manager.py: TTL önbelleğe alma ile oturum yaşam döngüsünü yönetir:

"""Session Manager — manages session lifecycle with TTL-based caching."""

import os
import time
from typing import Any

from google.adk.sessions import InMemorySessionService, VertexAiSessionService


class TTLCache:
    """Simple TTL cache for mapping A2A context_id to session_id."""

    def __init__(self, maxsize: int = 1000, ttl: int = 3600):
        self._cache: dict[str, tuple[Any, float]] = {}
        self._maxsize = maxsize
        self._ttl = ttl

    def get(self, key: str) -> Any | None:
        if key in self._cache:
            value, timestamp = self._cache[key]
            if time.time() - timestamp < self._ttl:
                return value
            else:
                del self._cache[key]
        return None

    def set(self, key: str, value: Any) -> None:
        if len(self._cache) >= self._maxsize:
            self._evict_oldest()
        self._cache[key] = (value, time.time())

    def _evict_oldest(self) -> None:
        if not self._cache:
            return
        sorted_keys = sorted(
            self._cache.keys(), key=lambda k: self._cache[k][1]
        )
        for key in sorted_keys[: max(1, len(sorted_keys) // 10)]:
            del self._cache[key]

    def __contains__(self, key: str) -> bool:
        return self.get(key) is not None


def create_session_service(
    project: str | None = None,
    location: str | None = None,
    agent_engine_id: str | None = None,
    use_vertex: bool | None = None,
) -> VertexAiSessionService | InMemorySessionService:
    """Create appropriate session service based on environment.

    Returns VertexAiSessionService for production, InMemorySessionService for local dev.
    """
    project = project or os.environ.get("GOOGLE_CLOUD_PROJECT")
    location = location or (
        os.environ.get("AGENT_ENGINE_LOCATION")
        or os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")
    )
    agent_engine_id = agent_engine_id or os.environ.get("AGENT_ENGINE_ID")

    if use_vertex is None:
        use_vertex = agent_engine_id is not None

    if use_vertex:
        if not project:
            raise ValueError("GOOGLE_CLOUD_PROJECT environment variable required")
        return VertexAiSessionService(
            project=project, location=location,
            agent_engine_id=agent_engine_id,
        )
    else:
        return InMemorySessionService()


class SessionManager:
    """Manages sessions with A2A context mapping and TTL caching."""

    def __init__(self, session_service=None, cache_maxsize=1000, cache_ttl=3600):
        self.session_service = session_service or create_session_service()
        self.session_cache = TTLCache(maxsize=cache_maxsize, ttl=cache_ttl)

    async def get_or_create_session(self, context_id, app_name, user_id):
        cached = self.session_cache.get(context_id)
        if cached:
            return cached
        session = await self.session_service.create_session(
            app_name=app_name, user_id=user_id,
        )
        self.session_cache.set(context_id, session.id)
        return session.id

Desen: Ortamdan haberdar hizmetler. Oturum ve bellek hizmetleri, Vertex AI arka uçları (Agent Engine dağıtımı için) ile bellek içi uygulamalar (yerel geliştirme için) arasında otomatik olarak geçiş yapar. Kod değişikliği gerekmez.

Şimdi bu hizmet dosyalarını diğer aracılar için kopyalayın. Bu dosyalar, aracıya özel bellek konularıyla aynı iskele kalıbını izler:

cp src/planner_agent/services/session_manager.py src/simulator_agent/services/session_manager.py
cp src/planner_agent/services/session_manager.py src/planner_agent/evaluator/services/session_manager.py

Proje yapısını doğrulama

find src -type f -name "*.py" | sort

Üç aracı paketinde de paylaşılan yapılandırmayı, bellek yöneticilerini, oturum yöneticilerini ve __init__.py dosyalarını görmeniz gerekir.

4. Değerlendirici aracısını oluşturma

Değerlendirici aracısı, sistemin kalite hakimidir. Özel LLM-as-Judge metrikleriyle Vertex AI Evaluation'ı kullanarak maraton planlarını 7 kritere göre puanlandırır. Gemini'ın değerlendirmesini yapılandırılmış değerlendirme ölçekleriyle birleştirmenin gücünü burada görebilirsiniz.

Değerlendirme şemalarını tanımlama

Oluşturma src/planner_agent/evaluator/schemas.py: Pydantic modelleri, yapılandırılmış çıkış biçimini tanımlar:

"""Schemas for the Evaluator Agent.

Defines structured output formats for plan evaluation.
"""

from pydantic import BaseModel, Field


EVALUATION_CRITERIA = [
    "safety_compliance",
    "community_impact",
    "logistics_completeness",
    "financial_viability",
    "participant_experience",
    "intent_alignment",
    "distance_compliance",
]


class EvaluationFinding(BaseModel):
    """A specific finding from plan evaluation."""

    criterion: str = Field(
        description=(
            f"Which evaluation criterion triggered this finding. "
            f"Must be one of: {', '.join(EVALUATION_CRITERIA)}"
        ),
    )
    severity: str = Field(
        description="Severity of the finding: 'high', 'medium', or 'low'",
    )
    description: str = Field(
        description="Description of the finding and why it matters",
    )


class EvaluationResult(BaseModel):
    """Structured output from the Evaluator Agent."""

    passed: bool = Field(
        description="Whether the plan passes evaluation (overall_score >= 85)",
    )
    overall_score: float = Field(
        ge=0.0, le=100.0,
        description="Weighted average score across all criteria (0.0 to 100.0)",
    )
    scores: dict[str, float] = Field(
        default_factory=dict,
        description="Per-criterion scores: {criterion_name: score}",
    )
    findings: list[EvaluationFinding] = Field(
        default_factory=list,
        description="List of specific evaluation findings",
    )
    improvement_suggestions: list[str] = Field(
        default_factory=list,
        description="Actionable suggestions to improve the plan score",
    )
    iteration_number: int = Field(
        default=1, ge=1,
        description="Which evaluation iteration this is",
    )
    summary: str = Field(
        default="",
        description="Brief natural-language summary of the evaluation",
    )

Aracıda output_schema=EvaluationResult kullanmak, her yanıtın bu yapıyla eşleşmesini garanti eder. Ayrıştırma gerekmez.

Değerlendirme talimatını yazma

Oluşturma src/planner_agent/evaluator/instruction.md:

You are the Evaluator Agent — the quality judge for marathon plans.

Your role is to evaluate marathon plans across multiple criteria and provide actionable feedback so the planning team can iteratively improve the plan. You use a multi-step "Chain of Thought" process to ensure your evaluation is thorough, fair, and constructive.

## Phase 1: Evaluation Methodology

When you receive a plan, evaluate it across the following 7 criteria. For each, you must derive a raw score (1-100) based on the specific checks below:

1. **Participant Experience (15% weight)**
   - **Checks**: Route scenic quality, landmarks, surface variety, amenities.
   - **Rubric**: 100: Outstanding landmarks/view; 1: Boring/unpleasant experience.

2. **Intent Alignment (10% weight)**
   - **Checks**: City match, theme match (scenic/fast), scale, budget goals.
   - **Rubric**: 100: Perfectly aligned; 1: Completely misaligned.

3. **Distance Compliance (5% weight)**
   - **Checks**: Exactly 26.2 miles or 42.195 km.
   - **Rubric**: 100: Exactly correct; 1: Wrong distance.

4. **Safety Compliance (20% weight)**
   - **Checks**: Emergency vehicle access, crowd management.
   - **Rubric**: 100: Fully safe, hospital access prioritized; 60: Some concerns; 1: Dangerous blockages.

5. **Logistics Completeness (20% weight)**
   - **Checks**: Timing systems, course marshals, infrastructure.
   - **Rubric**: 100: Comprehensive; 60: Significant gaps; 1: Critical logistics missing.

6. **Community Impact (15% weight)**
   - **Checks**: Noise disruption, neighborhood equity, residential/business access.
   - **Rubric**: 100: Community benefits/event integration; 1: Major negative impact.

7. **Financial Viability (15% weight)**
   - **Checks**: Budget balance, revenue (sponsorships), realistic cost estimates.
   - **Rubric**: 100: Strong/sustainable; 1: Major financial gaps/un-viable.

**Action**: Use the `evaluate_plan` tool to get computed scores from the **Vertex AI Evaluation API**. Use these scores as your baseline.

## Phase 2: Score Interpretation

- **Pass Threshold**: A plan passes ONLY if `overall_score >= 85` AND there are no High-Severity findings.
- **Severity Mapping**: Score < 40 → High, < 60 → Medium, < 80 → Low.

## Phase 3: Improvement Strategy

Generate actionable suggestions for any criterion scoring below 80. Provide 3-5 prioritized improvements. Be specific: "Add course marshals at miles 12, 14, and 16" instead of "Add more marshals".

Return a structured `EvaluationResult` with your assessment.

Oluşturma src/planner_agent/evaluator/prompts.py:

"""Prompts for the Evaluator Agent."""

import pathlib

_PROMPT_DIR = pathlib.Path(__file__).parent
INSTRUCTION = (_PROMPT_DIR / "instruction.md").read_text()

Vertex AI ile değerlendirme araçları oluşturma

Bu, Değerlendirici'nin temelini oluşturur: MetricPromptBuilder kullanılarak oluşturulan özel metrikler. Oluşturma src/planner_agent/evaluator/tools.py:

"""Tools for the Evaluator Agent.

Provides tools for evaluating marathon plans using Vertex AI Evaluation
with custom metrics. Uses a hybrid approach: LLM-based evaluation with
custom rubrics, plus deterministic checks, with heuristic fallback.
"""

import asyncio
import json
import os
import re
from typing import Any

import logging
import pandas as pd
import vertexai
from google.genai import types as genai_types
from vertexai import types
from .agent import CRITERION_WEIGHTS, SEVERITY_THRESHOLDS, MODEL
from .schemas import EvaluationResult


logger = logging.getLogger(__name__)

def _get_model_resource() -> str:
    """Get the full resource path for the Vertex AI evaluation model."""
    project_id = os.environ.get("GOOGLE_CLOUD_PROJECT")
    location = os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")
    return (
        f"projects/{project_id}/locations/{location}/publishers/google/models/{MODEL}"
        if project_id else MODEL
    )


# ============================================================================
# CUSTOM METRICS — each uses MetricPromptBuilder for structured rubrics
# ============================================================================


def _create_safety_compliance_metric() -> types.LLMMetric:
    """Evaluate emergency corridor access and crowd safety."""
    builder = types.MetricPromptBuilder(
        metric_definition=(
            "Evaluate whether the proposed marathon route maintains emergency "
            "vehicle access and crowd safety. Check for blocked hospitals, fire stations, "
            "and major emergency corridors."
        ),
        criteria={
            "Emergency corridor access": (
                "The route does not permanently block access to hospitals, "
                "fire stations, or police stations without providing clear detour routes."
            ),
            "Evacuation routes": (
                "Major evacuation routes remain accessible or have documented alternatives."
            ),
            "Emergency vehicle passage": (
                "The plan includes provisions for emergency vehicle crossing "
                "at regular intervals along the route."
            ),
        },
        rating_scores={
            "1": "Dangerous - Major emergency corridors blocked with no detours",
            "25": "Unsafe - Several emergency access points compromised",
            "50": "Concerning - Some emergency access issues that need resolution",
            "75": "Mostly safe - Minor emergency access concerns, easy to fix",
            "100": "Fully safe - Emergency access maintained throughout",
        },
    )
    return types.LLMMetric(
        name="safety_compliance",
        prompt_template=str(builder),
        judge_model=_get_model_resource(),
    )


def _create_community_impact_metric() -> types.LLMMetric:
    """Evaluate community disruption, inclusivity, and neighborhood equity."""
    builder = types.MetricPromptBuilder(
        metric_definition=(
            "Evaluate the marathon plan's impact on the local community. "
            "Check for noise disruption, residential access, business impact, "
            "inclusivity, and equitable neighborhood treatment."
        ),
        criteria={
            "Residential disruption": (
                "The plan minimizes disruption to residential areas, with "
                "reasonable timing and notification plans."
            ),
            "Business access": (
                "Local businesses along the route can still operate or have "
                "been accommodated with alternative access."
            ),
            "Neighborhood equity": (
                "The route and plan are accessible to diverse communities "
                "and do not disproportionately burden any demographic group."
            ),
            "Community engagement": (
                "The plan includes cheer zones, community events, or other "
                "ways to involve and benefit local residents."
            ),
        },
        rating_scores={
            "1": "Harmful - Major negative impact on community with no mitigation",
            "25": "Disruptive - Significant community issues that need resolution",
            "50": "Mixed - Some community concerns that need attention",
            "75": "Considerate - Minor community impacts with good mitigation",
            "100": "Excellent - Community benefits from the event",
        },
    )
    return types.LLMMetric(
        name="community_impact",
        prompt_template=str(builder),
        judge_model=_get_model_resource(),
    )


def _create_logistics_completeness_metric() -> types.LLMMetric:
    """Evaluate logistics coverage: water stations, timing, marshals."""
    builder = types.MetricPromptBuilder(
        metric_definition=(
            "Evaluate whether the marathon plan's logistics are complete and "
            "adequate for the expected scale."
        ),
        criteria={
            "Water and nutrition": "Hydration and nutrition are considered for the expected participant count.",
            "Timing and tracking": "Timing systems (chip timing, mats) are specified.",
            "Course management": "Sufficient course marshals, signage, barriers, and traffic control are planned.",
            "Start/finish infrastructure": "Start/finish areas have adequate capacity for the participant count.",
        },
        rating_scores={
            "1": "Incomplete - Critical logistics missing",
            "25": "Sparse - Major logistics gaps",
            "50": "Partial - Some logistics covered but significant gaps remain",
            "75": "Mostly complete - Minor logistics details to finalize",
            "100": "Comprehensive - All logistics thoroughly planned",
        },
    )
    return types.LLMMetric(
        name="logistics_completeness",
        prompt_template=str(builder),
        judge_model=_get_model_resource(),
    )


def _create_financial_viability_metric() -> types.LLMMetric:
    """Evaluate budget balance, revenue sources, and cost control."""
    builder = types.MetricPromptBuilder(
        metric_definition=(
            "Evaluate the financial viability of the marathon plan. "
            "Check budget balance, revenue sources, cost estimates, and sustainability."
        ),
        criteria={
            "Budget balance": "Revenue projections meet or exceed estimated costs.",
            "Cost estimates": "Cost estimates are realistic and cover major expense categories.",
            "Revenue diversity": "Revenue comes from multiple sources, not just registration fees.",
        },
        rating_scores={
            "1": "Unviable - Major financial gaps",
            "25": "Risky - Budget concerns that could threaten viability",
            "50": "Uncertain - Financial plan needs more detail",
            "75": "Viable - Sound financial plan with minor gaps",
            "100": "Strong - Well-balanced budget with diverse revenue streams",
        },
    )
    return types.LLMMetric(
        name="financial_viability",
        prompt_template=str(builder),
        judge_model=_get_model_resource(),
    )


def _create_participant_experience_metric() -> types.LLMMetric:
    """Evaluate route quality, scenic value, and runner amenities."""
    builder = types.MetricPromptBuilder(
        metric_definition=(
            "Evaluate the participant experience of a proposed marathon plan."
        ),
        criteria={
            "Route Quality": "The route surface is suitable and elevation profile appropriate.",
            "Scenic Value": "The route passes through interesting or landmark areas.",
            "Runner Amenities": "Post-race amenities (food, medals, recovery area) are planned.",
        },
        rating_scores={
            "1": "Poor experience - Boring route, no amenities",
            "50": "Average - Adequate but unremarkable",
            "100": "Excellent - Outstanding experience that runners will remember",
        },
    )
    return types.LLMMetric(
        name="participant_experience",
        prompt_template=str(builder),
        judge_model=_get_model_resource(),
    )


def _create_intent_alignment_metric() -> types.LLMMetric:
    """Evaluate whether the plan matches the user's original request."""
    builder = types.MetricPromptBuilder(
        metric_definition=(
            "Evaluate whether a proposed marathon plan matches the user's original intent."
        ),
        criteria={
            "City Match": "The plan takes place in the requested city.",
            "Theme Match": "The plan matches the desired theme (scenic, fast, charity, etc.).",
            "Scale Match": "The plan's scale matches the user's vision.",
        },
        rating_scores={
            "1": "Completely misaligned",
            "50": "Partially aligned - Key requirements missed",
            "100": "Perfectly aligned - All requirements addressed",
        },
    )
    return types.LLMMetric(
        name="intent_alignment",
        prompt_template=str(builder),
        judge_model=_get_model_resource(),
    )


def _check_distance_compliance_logic(response_text: str) -> dict:
    """Deterministic check for 26.2 mile (42.195 km) marathon distance."""
    text_lower = response_text.lower()
    score = 100.0
    issues = []

    for distance_str in re.findall(r'(\d+(?:\.\d+)?)\s*(?:miles?|mi)\b', text_lower):
        distance = float(distance_str)
        if 20 <= distance <= 30:
            deviation = abs(distance - 26.2)
            if deviation > 0.5:
                score = 1.0
                issues.append(f"Route distance {distance} miles deviates from 26.2 mile standard")
            elif deviation > 0.1:
                score = 60.0
                issues.append(f"Route distance {distance} miles is close but not exactly 26.2 miles")

    for distance_str in re.findall(r'(\d+(?:\.\d+)?)\s*(?:kilometers?|km)\b', text_lower):
        distance = float(distance_str)
        if 35 <= distance <= 50 and abs(distance - 42.195) > 1.0:
            score = 1.0
            issues.append(f"Route distance {distance} km deviates from 42.195 km standard")

    return {"score": score, "explanation": "; ".join(issues) if issues else "No distance issues detected"}


def _create_distance_compliance_metric() -> types.Metric:
    """Deterministic check for marathon distance — no LLM needed."""
    def check_distance_compliance(instance: dict) -> dict:
        response_text = instance["response"]["parts"][0]["text"]
        return _check_distance_compliance_logic(response_text)
    return types.Metric(
        name="distance_compliance",
        custom_function=check_distance_compliance,
    )


# ============================================================================
# MAIN EVALUATION TOOL
# ============================================================================


async def evaluate_plan(evaluation_request: str) -> dict[str, Any]:
    """Evaluate a proposed marathon plan across quality criteria.

    Uses Vertex AI Evaluation with 7 custom metrics.
    Falls back to heuristic evaluation if API fails.

    Args:
        evaluation_request: JSON string with user_intent and proposed_plan

    Returns:
        dict with evaluation results
    """
    try:
        request_data = json.loads(evaluation_request)
    except json.JSONDecodeError as e:
        return {
            "passed": False, "scores": {}, "overall_score": 0.0,
            "findings": [{"criterion": "intent_alignment",
                          "description": f"Invalid JSON input: {e}", "severity": "high"}],
            "improvement_suggestions": ["Provide valid JSON with 'user_intent' and 'proposed_plan' fields."],
            "eval_method": "error",
        }

    user_intent_raw = request_data.get("user_intent", "Unknown intent")
    proposed_plan_raw = request_data.get("proposed_plan", "No plan provided")
    user_intent = json.dumps(user_intent_raw) if not isinstance(user_intent_raw, str) else user_intent_raw
    proposed_plan = json.dumps(proposed_plan_raw) if not isinstance(proposed_plan_raw, str) else proposed_plan_raw

    project_id = os.environ.get("GOOGLE_CLOUD_PROJECT")
    location = os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")

    if project_id:
        try:
            scores, details = await _run_custom_eval(
                project_id=project_id, location=location,
                user_intent=user_intent, proposed_plan=proposed_plan,
            )
            return _build_result(scores, details, eval_method="vertex_ai_eval")
        except Exception as e:
            logger.warning(f"Vertex AI Eval failed, using heuristics: {e}")

    scores, details = _heuristic_eval(user_intent, proposed_plan)
    return _build_result(scores, details, eval_method="heuristic")


async def _run_custom_eval(
    project_id: str, location: str,
    user_intent: str, proposed_plan: str,
) -> tuple[dict[str, float], dict[str, str]]:
    """Run Vertex AI Evaluation with custom metrics."""
    vertexai.init(project=project_id, location=location)
    client = vertexai.Client(
        project=project_id, location=location,
        http_options=genai_types.HttpOptions(api_version="v1beta1"),
    )
    df = pd.DataFrame({"prompt": [user_intent], "response": [proposed_plan]})
    metrics = [
        _create_safety_compliance_metric(),
        _create_community_impact_metric(),
        _create_logistics_completeness_metric(),
        _create_financial_viability_metric(),
        _create_participant_experience_metric(),
        _create_intent_alignment_metric(),
        _create_distance_compliance_metric(),
    ]
    result = client.evals.evaluate(dataset=df, metrics=metrics)

    scores, details = {}, {}
    for case in result.eval_case_results:
        for cand in case.response_candidate_results:
            for metric_name, metric_result in cand.metric_results.items():
                raw_score = metric_result.score if hasattr(metric_result, "score") and metric_result.score is not None else 50.0
                scores[metric_name] = round(float(raw_score), 2)
                if hasattr(metric_result, "explanation") and metric_result.explanation:
                    details[metric_name] = metric_result.explanation
    return scores, details


def _heuristic_eval(user_intent: str, proposed_plan: str) -> tuple[dict[str, float], dict[str, str]]:
    """Heuristic evaluation fallback when Vertex AI Eval is unavailable."""
    plan_lower = proposed_plan.lower()
    scores, details = {}, {}

    # Safety
    safety_score = 80.0
    if "emergency" in plan_lower and "no detour" in plan_lower:
        safety_score = 20.0
    if "emergency vehicle" in plan_lower:
        safety_score = min(safety_score + 10.0, 100.0)
    scores["safety_compliance"] = safety_score
    details["safety_compliance"] = "Heuristic safety check"

    # Community
    community_score = 75.0
    if any(kw in plan_lower for kw in ["cheer zone", "community"]):
        community_score = min(community_score + 10.0, 100.0)
    scores["community_impact"] = community_score
    details["community_impact"] = "Heuristic community check"

    # Logistics
    logistics_score = 60.0
    for kw in ["hydration", "timing", "chip", "marshal"]:
        if kw in plan_lower:
            logistics_score += 10.0
    scores["logistics_completeness"] = min(logistics_score, 100.0)
    details["logistics_completeness"] = "Heuristic logistics check"

    # Financial
    financial_score = 65.0
    for kw in ["budget", "cost", "revenue", "sponsor"]:
        if kw in plan_lower:
            financial_score += 8.0
    scores["financial_viability"] = min(financial_score, 100.0)
    details["financial_viability"] = "Heuristic financial check"

    # Experience
    experience_score = 70.0
    for kw in ["scenic", "landmark", "medal", "post-race"]:
        if kw in plan_lower:
            experience_score += 8.0
    scores["participant_experience"] = min(experience_score, 100.0)
    details["participant_experience"] = "Heuristic experience check"

    # Intent
    scores["intent_alignment"] = 70.0
    details["intent_alignment"] = "Heuristic intent check"

    # Distance
    distance_result = _check_distance_compliance_logic(proposed_plan)
    scores["distance_compliance"] = float(distance_result["score"])
    details["distance_compliance"] = distance_result["explanation"]

    return scores, details


def _build_result(scores, details, eval_method):
    """Build the final evaluation result from scores and details."""
    findings, improvement_suggestions = [], []
    for criterion, score in scores.items():
        if score < 80.0:
            severity = "high" if score < SEVERITY_THRESHOLDS["high"] else "medium" if score < SEVERITY_THRESHOLDS["medium"] else "low"
            findings.append({"criterion": criterion, "description": details.get(criterion, f"Score: {score}"), "severity": severity})
            improvement_suggestions.append(_suggest_improvement(criterion, score, details.get(criterion, "")))

    overall_score = round(sum(scores.get(c, 50.0) * w for c, w in CRITERION_WEIGHTS.items()), 2)
    passed = overall_score >= 85.0 and not any(f["severity"] == "high" for f in findings)
    return {"passed": passed, "scores": scores, "findings": findings,
            "improvement_suggestions": improvement_suggestions,
            "overall_score": overall_score, "eval_method": eval_method}


def _suggest_improvement(criterion, score, detail):
    suggestions = {
        "safety_compliance": "Add emergency vehicle crossing points every 2 miles and plan detour routes around hospitals.",
        "community_impact": "Include cheer zones, notify affected residents, and ensure equitable route distribution.",
        "logistics_completeness": "Add timing chip details, plan course marshal positions, and detail start/finish infrastructure.",
        "financial_viability": "Add budget breakdown with cost estimates and identify 3+ revenue sources.",
        "participant_experience": "Highlight scenic landmarks and detail post-race amenities (medals, food, recovery).",
        "intent_alignment": "Review the user's original request and ensure the plan matches their stated requirements.",
        "distance_compliance": "Verify the route is exactly 26.2 miles (42.195 km) for marathon certification.",
    }
    return suggestions.get(criterion, f"Improve {criterion} (current score: {score:.2f})")

Buradaki temel kalıp karma değerlendirmedir: Araç, önce LLM-as-Judge metrikleriyle Vertex AI Eval'i dener. API kullanılamıyorsa sezgisel kontrollere geri döner.

Değerlendirici aracısını bağlayın

Oluşturma src/planner_agent/evaluator/agent.py:

"""Evaluator Agent — scores marathon plans using Vertex AI Evaluation.

Uses gemini-3.1-pro-preview for best evaluation quality.
"""

import os

import vertexai
from google.adk.agents import LlmAgent
from google.adk.tools.preload_memory_tool import PreloadMemoryTool

from .prompts import INSTRUCTION
from .services.memory_manager import auto_save_memories
from .schemas import EvaluationResult

# Agent identity
AGENT_NAME = "evaluator_agent"
AGENT_DESCRIPTION = (
    "Evaluates marathon plans across multiple quality criteria using Vertex AI "
    "Evaluation with custom metrics. Acts as LLM-as-Judge to score plans and "
    "provide actionable feedback for iterative improvement."
)

# Model — use gemini-3.1-pro-preview for evaluation accuracy
MODEL = os.getenv("EVALUATOR_MODEL", "gemini-3.1-pro-preview")

# Structured output schema — guarantees every response matches EvaluationResult
OUTPUT_SCHEMA = EvaluationResult

# Criterion weights must sum to 1.0
CRITERION_WEIGHTS = {
    "safety_compliance": 0.20,
    "community_impact": 0.15,
    "logistics_completeness": 0.20,
    "financial_viability": 0.15,
    "participant_experience": 0.15,
    "intent_alignment": 0.10,
    "distance_compliance": 0.05,
}

SEVERITY_THRESHOLDS = {"high": 40.0, "medium": 60.0, "low": 80.0}

# Initialize Vertex AI
project_id = os.environ.get("GOOGLE_CLOUD_PROJECT")
location = os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")
if project_id:
    vertexai.init(project=project_id, location=location)

from google.genai.types import GenerateContentConfig, ThinkingConfig
from .tools import evaluate_plan

# Enable thinking for better evaluation reasoning
evaluator_config = GenerateContentConfig(max_output_tokens=4096)
if "pro" in MODEL:
    evaluator_config.thinking_config = ThinkingConfig(thinking_budget=1024)

evaluator_agent = LlmAgent(
    name="evaluator_agent",
    model=MODEL,
    description=AGENT_DESCRIPTION,
    static_instruction=INSTRUCTION,
    output_schema=OUTPUT_SCHEMA,
    generate_content_config=evaluator_config,
    include_contents='none',
    tools=[PreloadMemoryTool(), evaluate_plan],
    after_agent_callback=auto_save_memories,
)

root_agent = evaluator_agent

Şimdi src/planner_agent/evaluator/services/memory_manager.py konumunda Değerlendiricinin bellek yöneticisini oluşturun. Bu yönetici, değerlendirme geçmişini ve puanlama trendlerini izler:

"""Memory Manager for Evaluator Agent."""

import os
from typing import TYPE_CHECKING

from google.adk.memory import VertexAiMemoryBankService
from vertexai._genai.types import (
    MemoryBankCustomizationConfig,
    MemoryBankCustomizationConfigMemoryTopic as MemoryTopic,
    MemoryBankCustomizationConfigMemoryTopicCustomMemoryTopic as CustomMemoryTopic,
)

if TYPE_CHECKING:
    from google.adk.agents.callback_context import CallbackContext

EVALUATION_HISTORY = MemoryTopic(
    custom_memory_topic=CustomMemoryTopic(
        label="evaluation_history",
        description="""Track evaluation results: scores, pass/fail verdicts,
        findings count, and iteration numbers.""",
    )
)

SCORING_TRENDS = MemoryTopic(
    custom_memory_topic=CustomMemoryTopic(
        label="scoring_trends",
        description="""Track which criteria consistently score low and
        average scores by category.""",
    )
)


def create_memory_service(project=None, location=None, agent_engine_id=None):
    project = project or os.environ.get("GOOGLE_CLOUD_PROJECT")
    location = location or os.environ.get("AGENT_ENGINE_LOCATION") or os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")
    agent_engine_id = agent_engine_id or os.environ.get("AGENT_ENGINE_ID")
    if not project:
        raise ValueError("GOOGLE_CLOUD_PROJECT required")
    if not agent_engine_id:
        return None
    return VertexAiMemoryBankService(project=project, location=location, agent_engine_id=agent_engine_id)


async def auto_save_memories(callback_context: "CallbackContext") -> None:
    agent_engine_id = os.environ.get("AGENT_ENGINE_ID")
    if not agent_engine_id:
        return
    try:
        svc = VertexAiMemoryBankService(
            project=os.environ.get("GOOGLE_CLOUD_PROJECT"),
            location=os.environ.get("AGENT_ENGINE_LOCATION") or os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1"),
            agent_engine_id=agent_engine_id,
        )
        await svc.add_session_to_memory(callback_context._invocation_context.session)
    except Exception as e:
        print(f"Warning: Failed to save memories: {e}")

Son olarak, değerlendirici paketi dışa aktarma işlemlerini ayarlayın. src/planner_agent/evaluator/__init__.py yerine şunu kullanın:

try:
    from .agent import root_agent, AGENT_NAME, MODEL
    __all__ = ["root_agent", "AGENT_NAME", "MODEL"]
except Exception:
    __all__ = ["root_agent"]

Temel kavram: ThinkingConfig. Değerlendirici, Gemini'ın yanıt vermeden önce değerlendirme ölçütlerini gözden geçirmesine olanak tanımak için thinking_budget=1024 kullanır. Bu, biraz daha yüksek gecikme süresi karşılığında puanlama doğruluğunu artırır.

5. Simulation Controller aracısını oluşturma

Simülasyon denetleyicisi son bekçidir. Bir planın simülasyon çalıştırmak için yeterli veriye sahip olduğunu onaylamak üzere hızlı bir ön koşul kontrolü gerçekleştirir ve kaliteyi yeniden değerlendirmez.

Onay şemasını tanımlama

Oluşturma src/simulator_agent/agent/schemas.py:

"""Schemas for the Simulation Controller Agent."""

from pydantic import BaseModel, Field


class SimulationApproval(BaseModel):
    """Structured output from the Simulation Controller Agent."""

    approved: bool = Field(description="Whether the plan is approved for simulation")
    overall_readiness: float = Field(ge=0.0, le=1.0, description="Readiness score (0-1)")
    route_feasibility: str = Field(description="'feasible', 'marginal', or 'infeasible'")
    logistics_readiness: str = Field(description="'ready', 'partial', or 'not_ready'")
    safety_clearance: str = Field(description="'cleared', 'conditional', or 'blocked'")
    blockers: list[str] = Field(default_factory=list, description="Blocking issues")
    recommendations: list[str] = Field(default_factory=list, description="Non-blocking suggestions")
    summary: str = Field(default="", description="Summary of the decision")

Hazırlık denetimi aracını oluşturma

src/simulator_agent/agent/tools.py oluşturun (belirleyici bir yapılacaklar listesi, LLM gerekmez):

"""Tools for the Simulation Controller Agent."""

import re
from typing import Any


async def check_plan_readiness(plan_text: str) -> dict[str, Any]:
    """Check a marathon plan for simulation readiness.

    Performs deterministic checks on the plan text to identify
    missing elements and assess completeness.
    """
    plan_lower = plan_text.lower()

    checklist = {
        "distance_specified": bool(
            re.search(r'(\d+(?:\.\d+)?)\s*(?:miles?|mi|kilometers?|km)\b', plan_lower)
        ),
        "water_stations": "water station" in plan_lower,
        "medical_tents": any(kw in plan_lower for kw in ["medical tent", "first aid", "medical station"]),
        "timing_system": any(kw in plan_lower for kw in ["timing", "chip", "tracking"]),
        "start_finish": any(kw in plan_lower for kw in ["start line", "finish line", "start/finish", "starting area"]),
        "emergency_access": any(kw in plan_lower for kw in ["emergency", "ambulance", "evacuation"]),
        "budget_included": any(kw in plan_lower for kw in ["budget", "cost", "revenue", "expense"]),
        "timeline_included": any(kw in plan_lower for kw in ["schedule", "timeline", "race day", "setup"]),
    }

    passed = sum(checklist.values())
    total = len(checklist)

    return {
        "checklist": checklist,
        "missing_elements": [k for k, v in checklist.items() if not v],
        "readiness_score": round(passed / total, 2),
        "passed_checks": passed,
        "total_checks": total,
    }


def get_tools() -> list:
    return [check_plan_readiness]

ADK becerisini oluşturma

ADK becerileri, aracının çalışma zamanında yüklediği prosedürel bilgi sağlar. Oluşturma src/simulator_agent/skills/review-marathon-plan/SKILL.md:

---
name: review-marathon-plan
description: Step-by-step methodology for reviewing marathon plans for simulation readiness, covering route feasibility, logistics completeness, and safety clearance.
---

# Review Marathon Plan

## Purpose

Systematically confirm that a marathon plan is ready for simulation execution by verifying the presence of all required prerequisite data.

## Procedure

### Step 1: Prerequisite Data Verification

Use the `check_plan_readiness` tool to confirm the presence of:

1. **Route Data**: Waypoints, landmarks, distance (26.2 mi / 42.195 km), start/finish locations.
2. **Logistics Data**: Water stations, medical tents, timing systems, participant scale.
3. **Safety Data**: Emergency access routes, evacuation plans, crowd management.

### Step 2: Approval Decision

1. Set `approved=true` ONLY if all three data categories are present.
2. If any critical data is missing, set `approved=false` and list missing elements in `blockers`.
3. Provide `recommendations` for minor data gaps.

Simülasyon denetleyicisi aracısını bağlama

Oluşturma src/simulator_agent/agent/config.py:

"""Configuration for the Simulation Controller Agent."""

import os
from .schemas import SimulationApproval

AGENT_NAME = "simulator_agent"
AGENT_DESCRIPTION = (
    "Simulation Controller Agent. Reviews marathon plans for simulation readiness, "
    "assessing route feasibility, logistics completeness, and safety clearance."
)
MODEL = os.getenv("SIMULATOR_MODEL", "gemini-3-flash-preview")
OUTPUT_SCHEMA = SimulationApproval

Oluşturma src/simulator_agent/agent/prompts.py:

"""Prompts for the Simulation Controller Agent."""

INSTRUCTION = """You are the Simulation Controller Agent — the final gatekeeper before a marathon plan enters simulation.

Your role is to perform a fast "Simulation Prerequisite Check" on marathon plans. You do NOT evaluate quality (the Evaluator Agent does that). You simply confirm the plan has all data required for the simulation engine.

## Available ADK Skills

1. **review-marathon-plan** — Check for simulation prerequisites (Route, Logistics, Safety).

## Prerequisite Check

1. **Route Data**: Waypoints, distance (26.2 mi / 42.195 km), start/finish locations.
2. **Logistics Data**: Timing and registration infrastructure, participant count.
3. **Safety Clearance**: Emergency access, evacuation plan, crowd management.

## Approval Decision

- **Approved** (`approved=true`): All prerequisite data present.
- **Rejected** (`approved=false`): Critical data missing. List in `blockers`.

Always return a structured SimulationApproval."""

Oluşturma src/simulator_agent/agent/agent.py:

"""Simulation Controller Agent — reviews plans for simulation readiness."""

import os
import pathlib

import vertexai
from google.adk.agents import LlmAgent
from google.adk.skills import load_skill_from_dir
from google.adk.tools.preload_memory_tool import PreloadMemoryTool
from google.adk.tools.skill_toolset import SkillToolset

from ..services.memory_manager import auto_save_memories
from .config import AGENT_DESCRIPTION, AGENT_NAME, MODEL, OUTPUT_SCHEMA
from .prompts import INSTRUCTION
from .tools import get_tools

# Initialize Vertex AI
project_id = os.environ.get("GOOGLE_CLOUD_PROJECT")
location = os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")
if project_id:
    vertexai.init(project=project_id, location=location)

# Load the review-marathon-plan ADK Skill
_skills_dir = pathlib.Path(__file__).parent.parent / "skills"
_review_skill = load_skill_from_dir(_skills_dir / "review-marathon-plan")
_skill_toolset = SkillToolset(skills=[_review_skill])

_tools = [_skill_toolset, PreloadMemoryTool(), *get_tools()]

from google.genai.types import GenerateContentConfig, ThinkingConfig

_agent_kwargs = dict(
    name=AGENT_NAME,
    model=MODEL,
    description=AGENT_DESCRIPTION,
    static_instruction=INSTRUCTION,
    tools=_tools,
    generate_content_config=GenerateContentConfig(
        thinking_config=ThinkingConfig(thinking_budget=0),
        max_output_tokens=4096,
    ),
    after_agent_callback=auto_save_memories,
)
if OUTPUT_SCHEMA is not None:
    _agent_kwargs["output_schema"] = OUTPUT_SCHEMA

root_agent = LlmAgent(**_agent_kwargs)

Şimdi src/simulator_agent/services/memory_manager.py adresinde simülatörün bellek yöneticisini oluşturun:

"""Memory Manager for Simulation Controller Agent."""

import os
from typing import TYPE_CHECKING

from google.adk.memory import VertexAiMemoryBankService
from vertexai._genai.types import (
    MemoryBankCustomizationConfig,
    MemoryBankCustomizationConfigMemoryTopic as MemoryTopic,
    MemoryBankCustomizationConfigMemoryTopicCustomMemoryTopic as CustomMemoryTopic,
)

if TYPE_CHECKING:
    from google.adk.agents.callback_context import CallbackContext

APPROVAL_HISTORY = MemoryTopic(
    custom_memory_topic=CustomMemoryTopic(
        label="approval_history",
        description="""Track approval decisions: verdicts, readiness scores, common blockers.""",
    )
)

def create_memory_service(project=None, location=None, agent_engine_id=None):
    project = project or os.environ.get("GOOGLE_CLOUD_PROJECT")
    location = location or os.environ.get("AGENT_ENGINE_LOCATION") or os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")
    agent_engine_id = agent_engine_id or os.environ.get("AGENT_ENGINE_ID")
    if not project:
        raise ValueError("GOOGLE_CLOUD_PROJECT required")
    if not agent_engine_id:
        return None
    return VertexAiMemoryBankService(project=project, location=location, agent_engine_id=agent_engine_id)

async def auto_save_memories(callback_context: "CallbackContext") -> None:
    agent_engine_id = os.environ.get("AGENT_ENGINE_ID")
    if not agent_engine_id:
        return
    try:
        svc = VertexAiMemoryBankService(
            project=os.environ.get("GOOGLE_CLOUD_PROJECT"),
            location=os.environ.get("AGENT_ENGINE_LOCATION") or os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1"),
            agent_engine_id=agent_engine_id,
        )
        await svc.add_session_to_memory(callback_context._invocation_context.session)
    except Exception as e:
        print(f"Warning: Failed to save memories: {e}")

Paket dışa aktarma işlemlerini ayarlayın. src/simulator_agent/agent/__init__.py yerine şunu kullanın:

try:
    from .agent import root_agent
    from .config import AGENT_NAME, MODEL
    __all__ = ["root_agent", "AGENT_NAME", "MODEL"]
except Exception:
    pass

src/simulator_agent/__init__.py yerine şunu kullanın:

try:
    from .agent import root_agent
    from .agent.config import AGENT_NAME, MODEL
    __all__ = ["root_agent", "AGENT_NAME", "MODEL"]
except Exception:
    pass

6. Maraton Planlayıcı aracısını oluşturma

Maraton Planlayıcı, ana düzenleyicidir. Alan bilgisi için ADK Skills'i kullanır, kalite değerlendirmesini Evaluator alt aracısına devreder ve A2A aracılığıyla Simulation Controller'a bağlanır.

Planlayıcı şemasını ve yapılandırmasını tanımlama

Oluşturma src/planner_agent/agent/schemas.py:

"""Schemas for the Marathon Planner Agent."""

from pydantic import BaseModel, Field


class MarathonPlan(BaseModel):
    """Structured marathon plan output (used in Phase 2+)."""

    city: str = Field(description="City where the marathon takes place")
    date: str = Field(description="Event date in YYYY-MM-DD format")
    theme: str = Field(description="Marathon theme: scenic, fast, charity, etc.")
    participants: int = Field(ge=0, description="Expected number of participants")
    route_summary: str = Field(description="High-level route description")
    route_waypoints: list[str] = Field(default_factory=list, description="Ordered waypoints")
    distance_miles: float = Field(ge=0, description="Total route distance in miles")
    logistics_summary: str = Field(description="Key logistics overview")
    budget_summary: str = Field(description="Budget overview")
    key_risks: list[str] = Field(default_factory=list, description="Top risks and mitigations")

Oluşturma src/planner_agent/agent/config.py:

"""Configuration for the Marathon Planner Agent."""

import os

AGENT_NAME = "planner_agent"
AGENT_DESCRIPTION = (
    "Marathon Planner Agent — Lead Architect. "
    "Designs comprehensive city marathon plans with built-in expertise in route design, "
    "traffic management, community impact, and economics. Evaluates plans via the "
    "Evaluator Agent (A2A) and submits for approval to the Simulation Controller (A2A)."
)
MODEL = os.getenv("PLANNER_MODEL", "gemini-3-flash-preview")
# Phase 1: No structured output — agent returns free-form text
OUTPUT_SCHEMA = None

Planlayıcı talimatını yazma

Oluşturma src/planner_agent/agent/planner-instruction.md:

# Role
Marathon Planner Agent (city marathon event architect).
Goal: Design comprehensive marathon plan based on user constraints.

# ADK Skills (LOAD ONCE BEFORE PLANNING)
1. `route-planning`: Generate route using `plan_marathon_route`.
2. `plan-evaluation`: Analyze demographics, capacity, revenue, safety.

# Core Requirements
- Safety: Emergency corridors, traffic cover.
- Community: Local business, noise, inclusivity.
- Logistics: Start/finish capacity, restrooms, roads.
- Finances: Maximize revenue/sponsorships.
- Experience: Scenic, runner comfort.

# User Prerequisites
Clarify if missing: City, Date/Season, Theme, Scale (participants), Budget, Special constraints.

# Deliverables
1. Route Design: GeoJSON via `plan_marathon_route` tool.
2. Traffic: Closures, detours, mitigation.
3. Community: Engagement, cheer zones, noise.
4. Economics: Revenue, costs, sponsors.
5. Logistics: Porta-potties, capacity, timing.
6. Timeline: Setup to teardown, waves.
7. Risks: Weather, crowd, emergency.

# A2A Collaboration
1. **Evaluator (`evaluator_agent`)**:
   - Send plan for 7-criteria scoring.
   - SINGLE PASS ONLY. Do not call twice to verify successful fixes.
2. **Simulation Controller (`simulator_agent`)**:
   - Call once after evaluation completes (REGARDLESS OF SCORE).
   - Accept result, DO NOT call again.

# Workflow
1. Gather reqs.
2. Load skills (ONCE).
3. Call `plan_marathon_route`.
4. Complete design.
5. Send to Evaluator.
6. Send to Simulator.
7. Present final.

# Rules & Format
- Personality: Pragmatic, detail-oriented.

Oluşturma src/planner_agent/agent/prompts.py:

"""System instructions for the Marathon Planner Agent."""

import pathlib

_PROMPT_DIR = pathlib.Path(__file__).parent
INSTRUCTION = (_PROMPT_DIR / "planner-instruction.md").read_text()

Kimlik doğrulama modülünü oluşturma

Agent Engine'e yapılan A2A çağrıları için aracıların Google Cloud kimlik doğrulaması yapması gerekir. Oluşturma src/planner_agent/agent/auth.py:

"""Authentication utilities for Agent Engine A2A connections.

Provides Google Cloud auth for httpx requests to Agent Engine agents.
"""

import httpx
from google.auth import default
from google.auth.credentials import Credentials
from google.auth.transport.requests import Request as AuthRequest


class GoogleAuthRefresh(httpx.Auth):
    """Google Cloud Auth with lazy credential refresh.

    Credentials are initialized lazily on first request to avoid
    serialization issues with ADK agents.
    """

    def __init__(self, scopes: list[str] | None = None) -> None:
        self.scopes = scopes or ["https://www.googleapis.com/auth/cloud-platform"]
        self.credentials: Credentials | None = None
        self.auth_request: AuthRequest | None = None

    def auth_flow(self, request: httpx.Request):
        if self.credentials is None:
            self.credentials, _ = default(scopes=self.scopes)
            self.auth_request = AuthRequest()

        if not self.credentials.valid:
            self.credentials.refresh(self.auth_request)

        request.headers["Authorization"] = f"Bearer {self.credentials.token}"
        yield request

ADK becerilerini ekleme

Oluşturma src/planner_agent/skills/route-planning/SKILL.md:

---
name: route-planning
description:
  Generates high-fidelity marathon routes using road network data (Dijkstra's algorithm)
  and outputting GeoJSON for visualization.
---

# Route Planning Skill

**Goal:** Design a mathematically perfect 42.195 km marathon route using real road network data.

## Capabilities

- **Automated Route Generation**: Uses a built-in road network and Dijkstra's algorithm to calculate a certified 42.195 km route between specified landmarks.
- **GeoJSON Output**: Returns a standards-compliant GeoJSON FeatureCollection.

## Resources

### Tools (Python)
- `tools.py`: Contains the `plan_marathon_route`, `add_water_stations`, and `add_medical_tents` implementations.

### References
- `references/marathon_planning_guide.md`: Marathon standards, road width, traffic severity, landmarks.

Oluşturma src/planner_agent/skills/plan-evaluation/SKILL.md:

---
name: plan-evaluation
description:
  Evaluates the plan against financial, community, and logistical goals.
---

# Plan Evaluation Skill

**Goal:** Provide verification data for the event including capacity, budget, demographics, and nuisance checking.

## Resources

### References
- `references/evaluation_criteria.md`: Detailed scoring rubrics.

ADK becerileri, aracının çalışma zamanında SkillToolset üzerinden yüklediği alan bilgisini (referans belgeler, veri dosyaları, komut dosyaları) paketlemenize olanak tanır. Bu sayede, talimatlar kısa tutulurken temsilcilere ayrıntılı prosedür bilgisi verilir.

Planlayıcı araçlarını kablolama

Oluşturma src/planner_agent/agent/tools.py: Aracıların bağlandığı yerdir:

"""Tools for the Marathon Planner Agent.

Contains:
- A2A infrastructure (SerializableRemoteA2aAgent, URL helpers)
- Remote A2A agent creators for Evaluator and Simulation Controller
- get_tools() — assembles all tools including SkillToolset
"""

import logging
import os

import httpx
from google.adk.agents.remote_a2a_agent import RemoteA2aAgent
from google.adk.tools.agent_tool import AgentTool
from google.adk.tools.function_tool import FunctionTool
from a2a.client.client import ClientConfig as A2AClientConfig
from a2a.client.client_factory import ClientFactory as A2AClientFactory
from a2a.types import TransportProtocol as A2ATransport

from .auth import GoogleAuthRefresh
from ..evaluator.agent import root_agent as evaluator_agent

logger = logging.getLogger(__name__)

AGENT_TIMEOUT_SECONDS = 120


# ============================================================================
# A2A INFRASTRUCTURE
# ============================================================================


def _get_agent_a2a_endpoint(resource_name: str, default_port: int = 8080) -> str:
    """Construct A2A card endpoint URL from resource name or local address."""
    if resource_name.startswith("local"):
        port = resource_name.split(":")[1] if ":" in resource_name else default_port
        return f"http://127.0.0.1:{port}/.well-known/agent-card.json"

    parts = resource_name.split("/")
    try:
        location = parts[parts.index("locations") + 1]
        return f"https://{location}-aiplatform.googleapis.com/v1beta1/{resource_name}/a2a/v1/card"
    except (ValueError, IndexError):
        return resource_name


def _get_agent_a2a_url(resource_name: str) -> str | None:
    """Construct the regional A2A message URL from Agent Engine resource name."""
    if resource_name.startswith("local"):
        return None
    parts = resource_name.split("/")
    location = parts[parts.index("locations") + 1]
    return f"https://{location}-aiplatform.googleapis.com/v1beta1/{resource_name}/a2a"


class SerializableRemoteA2aAgent(RemoteA2aAgent):
    """RemoteA2aAgent with authentication and Agent Engine URL fix.

    Handles two Agent Engine issues:
    1. Creates httpx client lazily with Google Cloud auth
    2. Fixes agent card URL (Agent Engine returns global URL that 404s)
    """

    def __init__(self, *, a2a_url: str | None = None, **kwargs):
        super().__init__(**kwargs)
        self._a2a_url_override = a2a_url

    async def _ensure_httpx_client(self) -> httpx.AsyncClient:
        if self._httpx_client is None:
            self._httpx_client = httpx.AsyncClient(
                timeout=httpx.Timeout(timeout=AGENT_TIMEOUT_SECONDS),
                headers={"Content-Type": "application/json"},
                auth=GoogleAuthRefresh(),
            )
            self._httpx_client_needs_cleanup = True

        if self._a2a_client_factory is None:
            self._a2a_client_factory = A2AClientFactory(
                config=A2AClientConfig(
                    httpx_client=self._httpx_client,
                    streaming=False, polling=False,
                    supported_transports=[A2ATransport.http_json, A2ATransport.jsonrpc],
                )
            )
        return self._httpx_client

    async def _resolve_agent_card_from_url(self, url: str):
        card = await super()._resolve_agent_card_from_url(url)
        if self._a2a_url_override:
            logger.info(f"Overriding agent card URL: {card.url} → {self._a2a_url_override}")
            card.url = self._a2a_url_override
        return card


def create_evaluator_tool() -> AgentTool:
    """Create Evaluator Agent tool — local sub-agent, no A2A needed."""
    return AgentTool(agent=evaluator_agent)


def create_simulator_agent() -> RemoteA2aAgent:
    """Create remote connection to Simulation Controller Agent via A2A."""
    resource_name = os.environ.get("SIMULATOR_AGENT_RESOURCE_NAME")
    if not resource_name:
        raise ValueError("SIMULATOR_AGENT_RESOURCE_NAME environment variable must be set")

    endpoint = _get_agent_a2a_endpoint(resource_name, default_port=8089)
    logger.info(f"Creating Simulation Controller Agent connection: {endpoint}")

    return SerializableRemoteA2aAgent(
        name="simulator_agent",
        description=(
            "Simulation Controller Agent for marathon plans. "
            "Reviews plans for simulation readiness, assessing route feasibility, "
            "logistics completeness, and safety clearance."
        ),
        agent_card=endpoint,
        a2a_url=_get_agent_a2a_url(resource_name),
    )


def create_simulation_controller_tool() -> AgentTool:
    return AgentTool(agent=create_simulator_agent())


# ============================================================================
# TOOL EXPORT
# ============================================================================


def get_tools() -> list:
    """Return the tools for the Marathon Planner Agent."""
    import pathlib
    import importlib.util
    from google.adk.skills import load_skill_from_dir
    from google.adk.tools.preload_memory_tool import PreloadMemoryTool
    from google.adk.tools.skill_toolset import SkillToolset

    # Load ADK Skills
    skills_dir = pathlib.Path(__file__).parent.parent / "skills"
    skills = [
        load_skill_from_dir(skills_dir / name)
        for name in sorted(skills_dir.iterdir())
        if name.is_dir() and not name.name.startswith("_")
    ]
    skill_toolset = SkillToolset(skills=skills)

    # Load route-planning tools dynamically (handles hyphenated directory names)
    def load_tool_from_skill(skill_name, tool_name):
        skill_tool_path = skills_dir / skill_name / "tools.py"
        if not skill_tool_path.exists():
            return None
        try:
            spec = importlib.util.spec_from_file_location(f"{skill_name}.tools", skill_tool_path)
            module = importlib.util.module_from_spec(spec)
            spec.loader.exec_module(module)
            return getattr(module, tool_name, None)
        except Exception as e:
            logger.error(f"Error loading tool {tool_name} from {skill_name}: {e}")
            return None

    plan_marathon_route_func = load_tool_from_skill("route-planning", "plan_marathon_route")

    tools = [skill_toolset, PreloadMemoryTool()]

    if plan_marathon_route_func:
        tools.append(FunctionTool(func=plan_marathon_route_func))

    # Add local evaluator sub-agent
    tools.append(create_evaluator_tool())
    logger.info("Added local Evaluator tool")

    # Add remote Simulation Controller via A2A (if configured)
    if os.environ.get("SIMULATOR_AGENT_RESOURCE_NAME"):
        tools.append(create_simulation_controller_tool())
        logger.info("Added A2A Simulation Controller tool")

    return tools

get_tools() işlevi üç farklı araç deseni gösterir:

1. SkillToolset: ADK becerilerinden prosedür bilgisi yükler.
2. AgentTool: Değerlendiriciyi yerel bir alt ajan olarak sarmalar.
3. SerializableRemoteA2aAgent: Simülatörü A2A protokolü üzerinden bağlar.

Planlayıcı aracısını bağlama

Oluşturma src/planner_agent/agent/agent.py:

"""Marathon Planner Agent — LlmAgent wiring.

Wires config + prompts + tools + skills into the LlmAgent.
"""

import os

import vertexai
from google.adk.agents import LlmAgent

from .config import AGENT_NAME, AGENT_DESCRIPTION, MODEL, OUTPUT_SCHEMA
from .prompts import INSTRUCTION
from .tools import get_tools
from ..services.memory_manager import auto_save_memories

# Initialize Vertex AI
project_id = os.environ.get("GOOGLE_CLOUD_PROJECT")
location = os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")
if project_id:
    vertexai.init(project=project_id, location=location)

from google.genai.types import GenerateContentConfig, ThinkingConfig

agent_kwargs = dict(
    name=AGENT_NAME,
    model=MODEL,
    description=AGENT_DESCRIPTION,
    static_instruction=INSTRUCTION,
    tools=get_tools(),
    generate_content_config=GenerateContentConfig(
        thinking_config=ThinkingConfig(thinking_budget=2048),
    ),
    after_agent_callback=auto_save_memories,
)

if OUTPUT_SCHEMA is not None:
    agent_kwargs["output_schema"] = OUTPUT_SCHEMA

root_agent = LlmAgent(**agent_kwargs)

Paket dışa aktarma işlemlerini ayarlayın. src/planner_agent/agent/__init__.py yerine şunu kullanın:

from .agent import root_agent
from .config import AGENT_NAME, MODEL, AGENT_DESCRIPTION, OUTPUT_SCHEMA

__all__ = ["root_agent", "AGENT_NAME", "MODEL", "AGENT_DESCRIPTION", "OUTPUT_SCHEMA"]

src/planner_agent/__init__.py yerine şunu kullanın:

try:
    from .agent import root_agent
    from .agent.config import AGENT_NAME, MODEL
    __all__ = ["root_agent", "AGENT_NAME", "MODEL"]
except Exception:
    __all__ = ["root_agent"]

ThinkingConfig bütçesi: Planlayıcı, ayrıntılı planlama için thinking_budget=2048; Değerlendirici, değerlendirme gerekçelendirmesi için 1024; Simülasyon Aracı ise 0 (hızlı bir kontrol listesi için düşünmeye gerek yoktur) kullanır. Bütçeyi görev karmaşıklığıyla eşleştirin.

7. Çok aracılı sistemi dağıtma ve test etme

Şimdi her şeyi bir araya getirin: Her aracı için A2A sunucuları oluşturun, ardından Solo modunu (Planlayıcı + Değerlendirici) ve Tam Ekip modunu (üç aracın tamamı) test edin.

Planlayıcının A2A temsilci kartını oluşturma

Oluşturma src/planner_agent/runtime/agent_card.py:

"""A2A Agent Card for Marathon Planner Agent."""

from a2a.types import AgentCapabilities, AgentCard, AgentSkill
from vertexai.preview.reasoning_engines.templates.a2a import create_agent_card


def create_marathon_planner_card() -> AgentCard:
    skill = AgentSkill(
        id="plan_marathon",
        name="Plan City Marathon",
        description=(
            "Design a comprehensive city marathon plan by coordinating with "
            "specialist agents. Evaluates plan quality and returns an actionable plan."
        ),
        tags=["marathon", "planning", "orchestration", "multi-agent"],
        examples=[
            "Plan a scenic marathon through Las Vegas for 30,000 runners",
            "Design a charity marathon in Austin for October 2026",
        ],
    )

    card = create_agent_card(
        agent_name="planner_agent",
        description=(
            "Marathon Planner Agent — Lead Orchestrator that designs city marathon "
            "plans. Coordinates specialist agents via A2A. Evaluates plans with "
            "Vertex AI Eval. Powered by Gemini 3 Flash Preview."
        ),
        skills=[skill],
    )

    if card.capabilities is None:
        card.capabilities = AgentCapabilities(streaming=True)
    else:
        card.capabilities.streaming = True

    return card

Planlayıcının A2A yürütücüsünü oluşturma

Oluşturma src/planner_agent/runtime/agent_executor.py:

"""A2A Agent Executor for Marathon Planner Agent."""

import json
import os

import vertexai
from a2a.server.agent_execution import AgentExecutor, RequestContext
from a2a.server.events import EventQueue
from a2a.server.tasks import TaskUpdater
from a2a.types import TaskState, TextPart, UnsupportedOperationError
from a2a.utils import new_agent_text_message
from a2a.utils.errors import ServerError
from google.adk import Runner
from google.genai import types

from ..services.memory_manager import create_memory_service
from ..services.session_manager import SessionManager, create_session_service


class MarathonPlannerExecutor(AgentExecutor):
    """Execute requests via A2A protocol."""

    def __init__(self):
        self.agent = None
        self.runner = None
        self.session_manager: SessionManager | None = None

    def _init_agent(self) -> None:
        if self.agent is None:
            try:
                from planner_agent.agent import root_agent
            except ImportError:
                from ..agent import root_agent

            project_id = os.environ.get("GOOGLE_CLOUD_PROJECT")
            location = os.environ.get("AGENT_ENGINE_LOCATION") or os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")
            vertexai.init(project=project_id, location=location)
            self.agent = root_agent

        if self.runner is None:
            project_id = os.environ.get("GOOGLE_CLOUD_PROJECT")
            location = os.environ.get("AGENT_ENGINE_LOCATION") or os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")

            session_service = create_session_service(project=project_id, location=location)
            memory_service = create_memory_service(project=project_id, location=location)

            from google.adk.apps import App
            from google.adk.agents.context_cache_config import ContextCacheConfig

            app = App(
                name=self.agent.name,
                root_agent=self.agent,
                context_cache_config=ContextCacheConfig(
                    cache_intervals=10, ttl_seconds=3600, min_tokens=4096,
                )
            )
            self.runner = Runner(
                app=app,
                session_service=session_service,
                memory_service=memory_service,
            )

        if self.session_manager is None:
            self.session_manager = SessionManager(
                session_service=self.runner.session_service,
            )

    async def execute(self, context: RequestContext, event_queue: EventQueue) -> None:
        if self.agent is None:
            self._init_agent()

        user_id = (
            context.message.metadata.get("user_id")
            if context.message and context.message.metadata
            else "marathon_user"
        )

        updater = TaskUpdater(event_queue, context.task_id, context.context_id)
        if not hasattr(context, "current_task") or not context.current_task:
            await updater.submit()
        await updater.start_work()

        request_data = context.get_user_input()
        if not request_data:
            await updater.update_status(TaskState.failed, message=new_agent_text_message("No request data"), final=True)
            return

        try:
            from google.adk.runners import RunConfig
            from google.adk.agents.run_config import ToolThreadPoolConfig
            from google.genai.types import ContextWindowCompressionConfig

            await updater.update_status(TaskState.working, message=new_agent_text_message("Planning marathon..."))

            session_id = await self.session_manager.get_or_create_session(
                context_id=context.context_id, app_name=self.runner.app_name, user_id=user_id,
            )

            content = types.Content(role="user", parts=[types.Part(text=request_data)])

            final_event = None
            async for event in self.runner.run_async(
                session_id=session_id, user_id=user_id, new_message=content,
                run_config=RunConfig(
                    tool_thread_pool_config=ToolThreadPoolConfig(max_workers=4),
                    max_llm_calls=15,
                    context_window_compression=ContextWindowCompressionConfig(),
                )
            ):
                if event.is_final_response():
                    final_event = event

            if final_event and final_event.content and final_event.content.parts:
                text = "".join(p.text for p in final_event.content.parts if hasattr(p, "text") and p.text)
                if text:
                    await updater.add_artifact([TextPart(text=text)], name="marathon_plan")
                    await updater.complete()
                    return

            await updater.update_status(TaskState.failed, message=new_agent_text_message("Failed to generate plan"), final=True)
        except Exception as e:
            await updater.update_status(TaskState.failed, message=new_agent_text_message(f"Planning failed: {e}"), final=True)

    async def cancel(self, context: RequestContext, event_queue: EventQueue) -> None:
        raise ServerError(error=UnsupportedOperationError())

Yerel A2A sunucularını oluşturma

Oluşturma src/planner_agent/runtime/local_server.py:

"""Local A2A server for the Marathon Planner Agent.

Usage: uv run python -m src.planner_agent.runtime.local_server
"""

import os
from dotenv import load_dotenv
load_dotenv()
os.environ["GOOGLE_GENAI_USE_VERTEXAI"] = "true"

import asyncio
import uvicorn
from a2a.server.apps import A2AStarletteApplication
from a2a.server.request_handlers import DefaultRequestHandler
from a2a.server.tasks import InMemoryTaskStore
from a2a.types import TransportProtocol
from google.adk import Runner
from google.adk.a2a.executor.a2a_agent_executor import A2aAgentExecutor, A2aAgentExecutorConfig
from google.adk.sessions import InMemorySessionService

AGENT_PORT = 8084


def create_marathon_planner_server():
    from ..agent import root_agent
    from .agent_card import create_marathon_planner_card

    runner = Runner(
        app_name=root_agent.name,
        agent=root_agent,
        session_service=InMemorySessionService(),
    )

    executor = A2aAgentExecutor(runner=runner, config=A2aAgentExecutorConfig())
    handler = DefaultRequestHandler(agent_executor=executor, task_store=InMemoryTaskStore())

    card = create_marathon_planner_card()
    card.url = f"http://localhost:{AGENT_PORT}"
    card.preferred_transport = TransportProtocol.jsonrpc

    return A2AStarletteApplication(agent_card=card, http_handler=handler)


async def run_server():
    project = os.environ.get("GOOGLE_CLOUD_PROJECT")
    if not project:
        raise ValueError("GOOGLE_CLOUD_PROJECT must be set. Check .env file.")

    print("=" * 60)
    print("Starting Marathon Planner Agent Local A2A Server")
    print(f"Project: {project}")
    print("=" * 60)

    app = create_marathon_planner_server()
    config = uvicorn.Config(app.build(), host="127.0.0.1", port=AGENT_PORT, log_level="info", loop="none")

    print(f"Planner A2A server: http://127.0.0.1:{AGENT_PORT}")
    print(f"Agent card: http://127.0.0.1:{AGENT_PORT}/.well-known/agent-card.json")
    print("Press Ctrl+C to stop")
    print("=" * 60)

    server = uvicorn.Server(config)
    await server.serve()

if __name__ == "__main__":
    asyncio.run(run_server())

Oluşturma src/simulator_agent/runtime/agent_card.py:

"""A2A Agent Card for Simulation Controller Agent."""

from a2a.types import AgentCard, AgentSkill
from vertexai.preview.reasoning_engines.templates.a2a import create_agent_card


def create_simulation_controller_card() -> AgentCard:
    skill = AgentSkill(
        id="review_marathon_plan",
        name="Review Marathon Plan",
        description="Review a marathon plan for simulation readiness.",
        tags=["simulation", "review", "approval", "marathon"],
    )
    return create_agent_card(
        agent_name="simulator_agent",
        description="Simulation Controller Agent - Reviews marathon plans for simulation readiness.",
        skills=[skill],
    )

Planlayıcının yürütücüsüyle aynı kalıbı izleyerek src/simulator_agent/runtime/agent_executor.py oluşturun (simulator_agent için içe aktarma işlemlerini güncelleyin):

"""A2A Agent Executor for Simulation Controller Agent."""

import os
import vertexai
from a2a.server.agent_execution import AgentExecutor, RequestContext
from a2a.server.events import EventQueue
from a2a.server.tasks import TaskUpdater
from a2a.types import TaskState, TextPart, UnsupportedOperationError
from a2a.utils import new_agent_text_message
from a2a.utils.errors import ServerError
from google.adk import Runner
from google.genai import types

from ..services.memory_manager import create_memory_service
from ..services.session_manager import SessionManager, create_session_service


class SimulationControllerExecutor(AgentExecutor):
    def __init__(self):
        self.agent = None
        self.runner = None
        self.session_manager = None

    def _init_agent(self):
        if self.agent is None:
            try:
                from simulator_agent.agent import root_agent
            except ImportError:
                from ..agent.agent import root_agent
            project_id = os.environ.get("GOOGLE_CLOUD_PROJECT")
            location = os.environ.get("AGENT_ENGINE_LOCATION") or os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")
            vertexai.init(project=project_id, location=location)
            self.agent = root_agent

        if self.runner is None:
            project_id = os.environ.get("GOOGLE_CLOUD_PROJECT")
            location = os.environ.get("AGENT_ENGINE_LOCATION") or os.environ.get("GOOGLE_CLOUD_LOCATION", "us-central1")
            session_service = create_session_service(project=project_id, location=location)
            memory_service = create_memory_service(project=project_id, location=location)

            from google.adk.apps import App
            from google.adk.agents.context_cache_config import ContextCacheConfig
            app = App(name=self.agent.name, root_agent=self.agent,
                      context_cache_config=ContextCacheConfig(cache_intervals=10, ttl_seconds=3600, min_tokens=4096))
            self.runner = Runner(app=app, session_service=session_service, memory_service=memory_service)

        if self.session_manager is None:
            self.session_manager = SessionManager(session_service=self.runner.session_service)

    async def execute(self, context: RequestContext, event_queue: EventQueue):
        if self.agent is None:
            self._init_agent()
        user_id = context.message.metadata.get("user_id") if context.message and context.message.metadata else "planner_agent"
        updater = TaskUpdater(event_queue, context.task_id, context.context_id)
        if not hasattr(context, "current_task") or not context.current_task:
            await updater.submit()
        await updater.start_work()
        plan_data = context.get_user_input()
        if not plan_data:
            await updater.update_status(TaskState.failed, message=new_agent_text_message("No plan data"), final=True)
            return
        try:
            await updater.update_status(TaskState.working, message=new_agent_text_message("Reviewing plan..."))
            session_id = await self.session_manager.get_or_create_session(
                context_id=context.context_id, app_name=self.runner.app_name, user_id=user_id)
            content = types.Content(role="user", parts=[types.Part(text=plan_data)])
            final_event = None
            async for event in self.runner.run_async(session_id=session_id, user_id=user_id, new_message=content):
                if event.is_final_response():
                    final_event = event
            if final_event and final_event.content and final_event.content.parts:
                text = "".join(p.text for p in final_event.content.parts if hasattr(p, "text") and p.text)
                if text:
                    await updater.add_artifact([TextPart(text=text)], name="result")
                    await updater.complete()
                    return
            await updater.update_status(TaskState.failed, message=new_agent_text_message("Failed"), final=True)
        except Exception as e:
            await updater.update_status(TaskState.failed, message=new_agent_text_message(f"Review failed: {e}"), final=True)

    async def cancel(self, context, event_queue):
        raise ServerError(error=UnsupportedOperationError())

Oluşturma src/simulator_agent/runtime/local_server.py:

"""Local A2A server for the Simulation Controller Agent.

Usage: uv run python -m src.simulator_agent.runtime.local_server
"""

import asyncio
import logging
import os

os.environ["GOOGLE_GENAI_USE_VERTEXAI"] = "true"

import uvicorn
from a2a.server.apps import A2AStarletteApplication
from a2a.server.request_handlers import DefaultRequestHandler
from a2a.server.tasks import InMemoryTaskStore
from dotenv import load_dotenv

load_dotenv()

AGENT_PORT = 8089


async def run_server():
    from .agent_card import create_simulation_controller_card
    from .agent_executor import SimulationControllerExecutor

    project = os.environ.get("GOOGLE_CLOUD_PROJECT")
    if not project:
        raise ValueError("GOOGLE_CLOUD_PROJECT must be set.")

    print("=" * 60)
    print("Starting Simulation Controller Agent Local A2A Server")
    print("=" * 60)

    agent_card = create_simulation_controller_card()
    executor = SimulationControllerExecutor()
    handler = DefaultRequestHandler(agent_executor=executor, task_store=InMemoryTaskStore())

    app = A2AStarletteApplication(agent_card=agent_card, http_handler=handler)
    config = uvicorn.Config(app.build(), host="127.0.0.1", port=AGENT_PORT, log_level="info", loop="none")

    print(f"Simulator A2A server: http://127.0.0.1:{AGENT_PORT}")
    print(f"Agent card: http://127.0.0.1:{AGENT_PORT}/.well-known/agent.json")
    print("Press Ctrl+C to stop")
    print("=" * 60)

    server = uvicorn.Server(config)
    await server.serve()

if __name__ == "__main__":
    asyncio.run(run_server())

Test Solo modu: Planlayıcı + Değerlendirici

Planlayıcı, Solo modunda Değerlendirici'yi yerel bir alt aracı olarak kullanır. A2A gerekmez.

Planlayıcı sunucusunu başlatın:

uv run python -m src.planner_agent.runtime.local_server

Şuna benzer bir çıkış alırsınız:

============================================================
Starting Marathon Planner Agent Local A2A Server
Project: your-project-id
============================================================
Planner A2A server: http://127.0.0.1:8084
Agent card: http://127.0.0.1:8084/.well-known/agent-card.json
Press Ctrl+C to stop

Yeni bir Cloud Shell sekmesi açın ve test isteği gönderin:

curl -s http://127.0.0.1:8084/.well-known/agent-card.json | python3 -m json.tool

planner_agent adını ve plan_marathon becerisini içeren temsilci kartını görürsünüz.

Tam Ekip modunu test etme: A2A üzerinden üç temsilci

Tam Takım modunda, Simülasyon Denetleyicisi ayrı bir A2A sunucusu olarak çalışır.

İkinci Cloud Shell sekmenizde simülatör sunucusunu başlatın:

uv run python -m src.simulator_agent.runtime.local_server

Ardından, ilk sekmede Planlayıcı'yı bağlı Simülatör ile yeniden başlatın:

SIMULATOR_AGENT_RESOURCE_NAME=local:8089 uv run python -m src.planner_agent.runtime.local_server

Günlüklerde şunları görmeniz gerekir:

Added local Evaluator tool
Added A2A Simulation Controller tool

Planlayıcı artık her iki temsilciye de bağlı. Maraton planlama isteği aldığında:

1. ADK becerilerini kullanarak planı tasarlama
2. Değerlendiriciye (AgentTool aracılığıyla yerel alt acente) yetki verme
3. Simülasyon denetleyicisine gönderme (A2A üzerinden uzaktan aracı)
4. Değerlendirme puanları ve simülasyon onayıyla nihai planı sunma

Aynı kod, farklı yapılandırma. Solo ve Tam Takım modu yalnızca bir ortam değişkeni açma/kapatma işlemidir: SIMULATOR_AGENT_RESOURCE_NAME. Planlayıcı kodu değişmez.

8. Temizleme

Google Cloud hesabınızın sürekli olarak ücretlendirilmesini önlemek için bu codelab sırasında oluşturulan kaynakları temizleyin.

Her Cloud Shell sekmesinde Ctrl+C tuşuna basarak çalışan yerel sunucuları durdurun.

Bu codelab'de yalnızca Vertex AI API çağrıları (Cloud Run hizmetleri veya Agent Engine dağıtımları gibi kalıcı bulut kaynakları yok) kullanıldığından bulut kaynaklarının silinmesi gerekmez.

Projeyi yalnızca bu codelab için oluşturduysanız projeyi silebilirsiniz:

gcloud projects delete $PROJECT_ID

9. Tebrikler

Tebrikler! Üç yapay zeka aracısının A2A protokolü üzerinden birlikte çalıştığı, Vertex AI Evaluation tarafından desteklenen kalite kontrolüyle çoklu temsilci maraton planlama sistemi oluşturdunuz.

Öğrendikleriniz

• LlmAgent, ThinkingConfig ve yapılandırılmış Pydantic çıkışlarıyla Agent Development Kit (ADK) kullanarak yapay zeka temsilcileri oluşturma
• LLM-as-Judge puanlaması ve deterministik kontroller için MetricPromptBuilder kullanarak özel Vertex AI Evaluation metrikleri oluşturma
• Yerel yetki devri için AgentTool kullanarak temsilcileri alt temsilci olarak bağlama
• RemoteA2aAgent ve A2AStarletteApplication kullanarak A2A protokolü üzerinden uzak temsilcileri bağlama
• Prosedürel bilgi için ADK Skills (SkillToolset) ve oturumlar arası öğrenme için Memory Bank'ı (PreloadMemoryTool) kullanma