bigquery_agent_analytics_plugin - Agent Engine (Not Working)

[dvdeveloper]

Hello, I am trying to implement bigquery_agent_analytics_plugin using agent engine. The SA has the permissions, but I cannot get the logs to be recorded with this new implementation. Has anyone been able to resolve this? Cloud Run works, but not agent engine. I am leaving some sample code in case anyone can help me.

agent.py

from google.adk.agents.llm_agent import Agent
from google.adk.models.google_llm import Gemini
from log.registro  import before_agent_callback, after_agent_callback, before_model_callback, after_model_callback , after_tool_callback
from log.langfuse_config import prompt
from google.genai import types
from google.adk.tools.retrieval import VertexAiRagRetrieval
from vertexai import rag
from utils.template_utils import escape_template_variables
from google.adk.tools.bigquery import BigQueryToolset, BigQueryCredentialsConfig


# --- BigQuery Agent Analytics (logs) ---

import os
import logging
from google.adk.plugins.bigquery_agent_analytics_plugin import (
    BigQueryAgentAnalyticsPlugin,
    BigQueryLoggerConfig,
)

BQ_PROJECT_ID = "tXXXXXX"
BQ_DATASET_ID = "agent_engine"
BQ_TABLE_ID = "XXXXX"


bq_logging_plugin = BigQueryAgentAnalyticsPlugin(
    project_id=BQ_PROJECT_ID,
    dataset_id=BQ_DATASET_ID,
    table_id=BQ_TABLE_ID,
    location="us-central1",  # Especificar location explícitamente
)

"""
credentials, _ = google.auth.default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
bigquery_toolset = BigQueryToolset(
    credentials_config=BigQueryCredentialsConfig(credentials=credentials)
)
 """

PROMPT = escape_template_variables(prompt.prompt)
LANGFUSE_AGENT_NAME = prompt.config['AGENT_NAME']
MODEL_NAME = prompt.config['MODEL']

PROJECT_ID = prompt.config['PROJECT_ID']
LOCATION = prompt.config['LOCATION']
CORPUS_ID = prompt.config['CORPUS_ID']
RAG_CORPUS_ID = f"projects/{PROJECT_ID}/locations/{LOCATION}/ragCorpora/{CORPUS_ID}"

rag_tool = VertexAiRagRetrieval(
    name="BaseConocimiento",
    description="Base de datos de conocimiento asociado Dexter Agente",
    rag_resources=[rag.RagResource(rag_corpus=RAG_CORPUS_ID)],
)

root_agent = Agent(
    name=LANGFUSE_AGENT_NAME,
    model=MODEL_NAME,
    instruction=PROMPT,
    tools=[rag_tool],
    before_agent_callback=before_agent_callback,
    after_agent_callback=after_agent_callback,
    before_model_callback=before_model_callback,
    after_model_callback=after_model_callback,
    after_tool_callback=after_tool_callback,
)

update.py

import vertexai
from vertexai import agent_engines
from vertexai.preview import reasoning_engines
from agente.agent import root_agent, bq_logging_plugin
import uuid

vertexai.init(
    project="XXXXXXXXX",
    location="us-central1",
    staging_bucket="gs://XXXXXXXXX"
)

# AdkApp acepta agent y plugins por separado
app_adk = reasoning_engines.AdkApp(
    agent=root_agent,
    plugins=[bq_logging_plugin],
    enable_tracing=True,
)

gcs_dir_name = str(uuid.uuid4())
remote_app = agent_engines.update(
    agent_engine=app_adk,
    resource_name="projects/XXXXX/locations/us-central1/reasoningEngines/X06XXX68445XXX70XXXX",
    service_account="sa-agents@XXXXXX",
    requirements=[
        "google-adk>=1.21.0",
        "google-cloud-aiplatform[adk,agent_engines]>=1.21.0",
        "llama-index",
        "langfuse",
        "google-cloud-secret-manager",
        "google-cloud-firestore",
        "google-cloud-bigquery",
        "google-cloud-bigquery-storage",
        "google-cloud-storage",
    ],
    extra_packages=["./agente","./log","./utils"],
    gcs_dir_name=f"tXXXXX{gcs_dir_name}",
    env_vars={
        "GOOGLE_CLOUD_AGENT_ENGINE_ENABLE_TELEMETRY": "true",
        "OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT": "true",
    }
)