BigQuery Agent Analytics plugin for ADK

Supported in ADKPython v1.21.0Preview

Version Requirement

Use ADK Python version 1.26.0 or higher to make full use of the features described in this document, including auto-schema-upgrade, tool provenance tracking, and HITL event tracing.

The BigQuery Agent Analytics Plugin significantly enhances the Agent Development Kit (ADK) by providing a robust solution for in-depth agent behavior analysis. Using the ADK Plugin architecture and the BigQuery Storage Write API, it captures and logs critical operational events directly into a Google BigQuery table, empowering you with advanced capabilities for debugging, real-time monitoring, and comprehensive offline performance evaluation.

Version 1.26.0 adds Auto Schema Upgrade (safely add new columns to existing tables), Tool Provenance tracking (LOCAL, MCP, SUB_AGENT, A2A, TRANSFER_AGENT), and HITL Event Tracing for human-in-the-loop interactions.

Preview release

The BigQuery Agent Analytics Plugin is in Preview release. For more information, see the launch stage descriptions.

BigQuery Storage Write API

This feature uses BigQuery Storage Write API, which is a paid service. For information on costs, see the BigQuery documentation.

Use cases

• Agent workflow debugging and analysis: Capture a wide range of plugin lifecycle events (LLM calls, tool usage) and agent-yielded events (user input, model responses), into a well-defined schema.
• High-volume analysis and debugging: Logging operations are performed asynchronously using the Storage Write API to allow high throughput and low latency.
• Multimodal Analysis: Log and analyze text, images, and other modalities. Large files are offloaded to GCS, making them accessible to BigQuery ML via Object Tables.
• Distributed Tracing: Built-in support for OpenTelemetry-style tracing (trace_id, span_id) to visualize agent execution flows.
• Tool Provenance: Track the origin of each tool call (local function, MCP server, sub-agent, A2A remote agent, or transfer agent).
• Human-in-the-Loop (HITL) Tracing: Dedicated event types for credential requests, confirmation prompts, and user input requests.

The agent event data recorded varies based on the ADK event type. For more information, see Event types and payloads.

Prerequisites

• Google Cloud Project with the BigQuery API enabled.
• BigQuery Dataset: Create a dataset to store logging tables before using the plugin. The plugin automatically creates the necessary events table within the dataset if the table does not exist.
• Google Cloud Storage Bucket (Optional): If you plan to log multimodal content (images, audio, etc.), creating a GCS bucket is recommended for offloading large files.
• Authentication:
  • Local: Run gcloud auth application-default login.
  • Cloud: Ensure your service account has the required permissions.

IAM permissions

For the agent to work properly, the principal (e.g., service account, user account) under which the agent is running needs these Google Cloud roles: * roles/bigquery.jobUser at Project Level to run BigQuery queries. * roles/bigquery.dataEditor at Table Level to write log/event data. * If using GCS offloading: roles/storage.objectCreator and roles/storage.objectViewer on the target bucket.

Use with agent

You use the BigQuery Agent Analytics Plugin by configuring and registering it with your ADK agent's App object. The following example shows an implementation of an agent with this plugin, including GCS offloading:

my_bq_agent/agent.py

# my_bq_agent/agent.py
import os
import google.auth
from google.adk.apps import App
from google.adk.plugins.bigquery_agent_analytics_plugin import BigQueryAgentAnalyticsPlugin, BigQueryLoggerConfig
from google.adk.agents import Agent
from google.adk.models.google_llm import Gemini
from google.adk.tools.bigquery import BigQueryToolset, BigQueryCredentialsConfig


# --- OpenTelemetry TracerProvider Setup (Optional) ---
# ADK includes OpenTelemetry as a core dependency.
# Configuring a TracerProvider enables full distributed tracing
# (populates trace_id, span_id with standard OTel identifiers).
# If no TracerProvider is configured, the plugin falls back to internal
# UUIDs for span correlation while still preserving the parent-child hierarchy.
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
trace.set_tracer_provider(TracerProvider())

# --- Configuration ---
PROJECT_ID = os.environ.get("GOOGLE_CLOUD_PROJECT", "your-gcp-project-id")
DATASET_ID = os.environ.get("BIG_QUERY_DATASET_ID", "your-big-query-dataset-id")
LOCATION = os.environ.get("GOOGLE_CLOUD_LOCATION", "US") # default location is US in the plugin
GCS_BUCKET = os.environ.get("GCS_BUCKET_NAME", "your-gcs-bucket-name") # Optional

if PROJECT_ID == "your-gcp-project-id":
    raise ValueError("Please set GOOGLE_CLOUD_PROJECT or update the code.")

# --- CRITICAL: Set environment variables BEFORE Gemini instantiation ---
os.environ['GOOGLE_CLOUD_PROJECT'] = PROJECT_ID
os.environ['GOOGLE_CLOUD_LOCATION'] = LOCATION
os.environ['GOOGLE_GENAI_USE_VERTEXAI'] = 'True'

# --- Initialize the Plugin with Config ---
bq_config = BigQueryLoggerConfig(
    enabled=True,
    gcs_bucket_name=GCS_BUCKET, # Enable GCS offloading for multimodal content
    log_multi_modal_content=True,
    max_content_length=500 * 1024, # 500 KB limit for inline text
    batch_size=1, # Default is 1 for low latency, increase for high throughput
    shutdown_timeout=10.0
)

bq_logging_plugin = BigQueryAgentAnalyticsPlugin(
    project_id=PROJECT_ID,
    dataset_id=DATASET_ID,
    table_id="agent_events", # default table name is agent_events
    config=bq_config,
    location=LOCATION
)

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

llm = Gemini(model="gemini-2.5-flash")

root_agent = Agent(
    model=llm,
    name='my_bq_agent',
    instruction="You are a helpful assistant with access to BigQuery tools.",
    tools=[bigquery_toolset]
)

# --- Create the App ---
app = App(
    name="my_bq_agent",
    root_agent=root_agent,
    plugins=[bq_logging_plugin],
)

Run and test agent

Test the plugin by running the agent and making a few requests through the chat interface, such as "tell me what you can do" or "List datasets in my cloud project ". These actions create events which are recorded in your Google Cloud project BigQuery instance. Once these events have been processed, you can view the data for them in the BigQuery Console, using this query

SELECT timestamp, event_type, content
FROM `your-gcp-project-id.your-big-query-dataset-id.agent_events`
ORDER BY timestamp DESC
LIMIT 20;

Tracing and Observability

The plugin supports OpenTelemetry for distributed tracing. OpenTelemetry is included as a core dependency of ADK and is always available.

• Automatic Span Management: The plugin automatically generates spans for Agent execution, LLM calls, and Tool executions.
• OpenTelemetry Integration: If a TracerProvider is configured (as shown in the example above), the plugin will use valid OTel spans, populating trace_id, span_id, and parent_span_id with standard OTel identifiers. This allows you to correlate agent logs with other services in your distributed system.
• Fallback Mechanism: If no TracerProvider is configured (i.e., only the default no-op provider is active), the plugin automatically falls back to generating internal UUIDs for spans and uses the invocation_id as the trace ID. This ensures that the parent-child hierarchy (Agent -> Span -> Tool/LLM) is always preserved in the BigQuery logs, even without a configured TracerProvider.

Configuration options

You can customize the plugin using BigQueryLoggerConfig.

• enabled (bool, default: True): To disable the plugin from logging agent data to the BigQuery table, set this parameter to False.
• table_id (str, default: "agent_events"): The BigQuery table ID within the dataset. Can also be overridden by the table_id parameter on the BigQueryAgentAnalyticsPlugin constructor, which takes precedence.
• clustering_fields (List[str], default: ["event_type", "agent", "user_id"]): The fields used to cluster the BigQuery table when it is automatically created.
• gcs_bucket_name (Optional[str], default: None): The name of the GCS bucket to offload large content (images, blobs, large text) to. If not provided, large content may be truncated or replaced with placeholders.
• connection_id (Optional[str], default: None): The BigQuery connection ID (e.g., us.my-connection) to use as the authorizer for ObjectRef columns. Required for using ObjectRef with BigQuery ML.
• max_content_length (int, default: 500 * 1024): The maximum length (in characters) of text content to store inline in BigQuery before offloading to GCS (if configured) or truncating. Default is 500 KB.
• batch_size (int, default: 1): The number of events to batch before writing to BigQuery.
• batch_flush_interval (float, default: 1.0): The maximum time (in seconds) to wait before flushing a partial batch.
• shutdown_timeout (float, default: 10.0): Seconds to wait for logs to flush during shutdown.
• event_allowlist (Optional[List[str]], default: None): A list of event types to log. If None, all events are logged except those in event_denylist. For a comprehensive list of supported event types, refer to the Event types and payloads section.
• event_denylist (Optional[List[str]], default: None): A list of event types to skip logging. For a comprehensive list of supported event types, refer to the Event types and payloads section.
• content_formatter (Optional[Callable[[Any, str], Any]], default: None): An optional function to format event content before logging. The function receives two arguments: the raw content and the event type string (e.g., "LLM_REQUEST").
• log_multi_modal_content (bool, default: True): Whether to log detailed content parts (including GCS references).
• queue_max_size (int, default: 10000): The maximum number of events to hold in the in-memory queue before dropping new events.
• retry_config (RetryConfig, default: RetryConfig()): Configuration for retrying failed BigQuery writes (attributes: max_retries, initial_delay, multiplier, max_delay).
• log_session_metadata (bool, default: True): If True, logs session information into the attributes column, including session_id, app_name, user_id, and the session state dictionary (e.g., custom state like gchat thread-id, customer_id).
• custom_tags (Dict[str, Any], default: {}): A dictionary of static tags (e.g., {"env": "prod", "version": "1.0"}) to be included in the attributes column for every event.
• auto_schema_upgrade (bool, default: True): When enabled, the plugin automatically adds new columns to an existing table when the plugin schema evolves. Only additive changes are made (columns are never dropped or altered). A version label (adk_schema_version) on the table ensures the diff runs at most once per schema version. Safe to leave enabled.

The following code sample shows how to define a configuration for the BigQuery Agent Analytics plugin:

import json
import re

from google.adk.plugins.bigquery_agent_analytics_plugin import BigQueryLoggerConfig

def redact_dollar_amounts(event_content: Any, event_type: str) -> str:
    """
    Custom formatter to redact dollar amounts (e.g., $600, $12.50)
    and ensure JSON output if the input is a dict.

    Args:
        event_content: The raw content of the event.
        event_type: The event type string (e.g., "LLM_REQUEST", "LLM_RESPONSE").
    """
    text_content = ""
    if isinstance(event_content, dict):
        text_content = json.dumps(event_content)
    else:
        text_content = str(event_content)

    # Regex to find dollar amounts: $ followed by digits, optionally with commas or decimals.
    # Examples: $600, $1,200.50, $0.99
    redacted_content = re.sub(r'\$\d+(?:,\d{3})*(?:\.\d+)?', 'xxx', text_content)

    return redacted_content

config = BigQueryLoggerConfig(
    enabled=True,
    event_allowlist=["LLM_REQUEST", "LLM_RESPONSE"], # Only log these events
    # event_denylist=["TOOL_STARTING"], # Skip these events
    shutdown_timeout=10.0, # Wait up to 10s for logs to flush on exit
    max_content_length=500, # Truncate content to 500 chars
    content_formatter=redact_dollar_amounts, # Redact the dollar amounts in the logging content
    queue_max_size=10000, # Max events to hold in memory
    auto_schema_upgrade=True, # Automatically add new columns to existing tables
    # retry_config=RetryConfig(max_retries=3), # Optional: Configure retries
)

plugin = BigQueryAgentAnalyticsPlugin(..., config=config)

Schema and production setup

Schema Reference

The events table (agent_events) uses a flexible schema. The following table provides a comprehensive reference with example values.

Field Name	Type	Mode	Description	Example Value
timestamp	TIMESTAMP	REQUIRED	UTC timestamp of event creation. Acts as the primary ordering key and the daily partitioning key. Precision is microsecond.	2026-02-03 20:52:17 UTC
event_type	STRING	NULLABLE	The canonical event category. Standard values include LLM_REQUEST, LLM_RESPONSE, LLM_ERROR, TOOL_STARTING, TOOL_COMPLETED, TOOL_ERROR, AGENT_STARTING, AGENT_COMPLETED, STATE_DELTA, INVOCATION_STARTING, INVOCATION_COMPLETED, USER_MESSAGE_RECEIVED, and HITL events (see HITL events). Used for high-level filtering.	LLM_REQUEST
agent	STRING	NULLABLE	The name of the agent responsible for this event. Defined during agent initialization or via the root_agent_name context.	my_bq_agent
session_id	STRING	NULLABLE	A persistent identifier for the entire conversation thread. Stays constant across multiple turns and sub-agent calls.	04275a01-1649-4a30-b6a7-5b443c69a7bc
invocation_id	STRING	NULLABLE	The unique identifier for a single execution turn or request cycle. Corresponds to trace_id in many contexts.	e-b55b2000-68c6-4e8b-b3b3-ffb454a92e40
user_id	STRING	NULLABLE	The identifier of the user (human or system) initiating the session. Extracted from the User object or metadata.	test_user
trace_id	STRING	NULLABLE	The OpenTelemetry Trace ID (32-char hex). Links all operations within a single distributed request lifecycle.	e-b55b2000-68c6-4e8b-b3b3-ffb454a92e40
span_id	STRING	NULLABLE	The OpenTelemetry Span ID (16-char hex). Uniquely identifies this specific atomic operation.	69867a836cd94798be2759d8e0d70215
parent_span_id	STRING	NULLABLE	The Span ID of the immediate caller. Used to reconstruct the parent-child execution tree (DAG).	ef5843fe40764b4b8afec44e78044205
content	JSON	NULLABLE	The primary event payload. Structure is polymorphic based on event_type.	{"system_prompt": "You are...", "prompt": [{"role": "user", "content": "hello"}], "response": "Hi", "usage": {"total": 15}}
attributes	JSON	NULLABLE	Metadata/Enrichment (usage stats, model info, tool provenance, custom tags).	{"model": "gemini-2.5-flash", "usage_metadata": {"total_token_count": 15}, "session_metadata": {"session_id": "...", "app_name": "...", "user_id": "...", "state": {}}, "custom_tags": {"env": "prod"}}
latency_ms	JSON	NULLABLE	Performance metrics. Standard keys are total_ms (wall-clock duration) and time_to_first_token_ms (streaming latency).	{"total_ms": 1250, "time_to_first_token_ms": 450}
status	STRING	NULLABLE	High-level outcome. Values: OK (success) or ERROR (failure).	OK
error_message	STRING	NULLABLE	Human-readable exception message or stack trace fragment. Populated only when status is ERROR.	Error 404: Dataset not found
is_truncated	BOOLEAN	NULLABLE	true if content or attributes exceeded the BigQuery cell size limit (default 10MB) and were partially dropped.	false
content_parts	RECORD	REPEATED	Array of multi-modal segments (Text, Image, Blob). Used when content cannot be serialized as simple JSON (e.g., large binaries or GCS refs).	[{"mime_type": "text/plain", "text": "hello"}]

The plugin automatically creates the table if it does not exist. However, for production, we recommend creating the table manually using the following DDL, which utilizes the JSON type for flexibility and REPEATED RECORDs for multimodal content.

Recommended DDL:

CREATE TABLE `your-gcp-project-id.adk_agent_logs.agent_events`
(
  timestamp TIMESTAMP NOT NULL OPTIONS(description="The UTC time at which the event was logged."),
  event_type STRING OPTIONS(description="Indicates the type of event being logged (e.g., 'LLM_REQUEST', 'TOOL_COMPLETED')."),
  agent STRING OPTIONS(description="The name of the ADK agent or author associated with the event."),
  session_id STRING OPTIONS(description="A unique identifier to group events within a single conversation or user session."),
  invocation_id STRING OPTIONS(description="A unique identifier for each individual agent execution or turn within a session."),
  user_id STRING OPTIONS(description="The identifier of the user associated with the current session."),
  trace_id STRING OPTIONS(description="OpenTelemetry trace ID for distributed tracing."),
  span_id STRING OPTIONS(description="OpenTelemetry span ID for this specific operation."),
  parent_span_id STRING OPTIONS(description="OpenTelemetry parent span ID to reconstruct hierarchy."),
  content JSON OPTIONS(description="The event-specific data (payload) stored as JSON."),
  content_parts ARRAY<STRUCT<
    mime_type STRING,
    uri STRING,
    object_ref STRUCT<
      uri STRING,
      version STRING,
      authorizer STRING,
      details JSON
    >,
    text STRING,
    part_index INT64,
    part_attributes STRING,
    storage_mode STRING
  >> OPTIONS(description="Detailed content parts for multi-modal data."),
  attributes JSON OPTIONS(description="Arbitrary key-value pairs for additional metadata (e.g., 'root_agent_name', 'model_version', 'usage_metadata', 'session_metadata', 'custom_tags')."),
  latency_ms JSON OPTIONS(description="Latency measurements (e.g., total_ms)."),
  status STRING OPTIONS(description="The outcome of the event, typically 'OK' or 'ERROR'."),
  error_message STRING OPTIONS(description="Populated if an error occurs."),
  is_truncated BOOLEAN OPTIONS(description="Flag indicates if content was truncated.")
)
PARTITION BY DATE(timestamp)
CLUSTER BY event_type, agent, user_id;

Event types and payloads

The content column now contains a JSON object specific to the event_type. The content_parts column provides a structured view of the content, especially useful for images or offloaded data.

Content Truncation

• Variable content fields are truncated to max_content_length (configured in BigQueryLoggerConfig, default 500KB).
• If gcs_bucket_name is configured, large content is offloaded to GCS instead of being truncated, and a reference is stored in content_parts.object_ref.

LLM interactions (plugin lifecycle)

These events track the raw requests sent to and responses received from the LLM.

1. LLM_REQUEST

Captures the prompt sent to the model, including conversation history and system instructions.

{
  "event_type": "LLM_REQUEST",
  "content": {
    "system_prompt": "You are a helpful assistant...",
    "prompt": [
      {
        "role": "user",
        "content": "hello how are you today"
      }
    ]
  },
  "attributes": {
    "root_agent_name": "my_bq_agent",
    "model": "gemini-2.5-flash",
    "tools": ["list_dataset_ids", "execute_sql"],
    "llm_config": {
      "temperature": 0.5,
      "top_p": 0.9
    }
  }
}

2. LLM_RESPONSE

Captures the model's output and token usage statistics.

{
  "event_type": "LLM_RESPONSE",
  "content": {
    "response": "text: 'Hello! I'm doing well...'",
    "usage": {
      "completion": 19,
      "prompt": 10129,
      "total": 10148
    }
  },
  "attributes": {
    "root_agent_name": "my_bq_agent",
    "model_version": "gemini-2.5-flash-001",
    "usage_metadata": {
      "prompt_token_count": 10129,
      "candidates_token_count": 19,
      "total_token_count": 10148
    }
  },
  "latency_ms": {
    "time_to_first_token_ms": 2579,
    "total_ms": 2579
  }
}

3. LLM_ERROR

Logged when an LLM call fails with an exception. The error message is captured and the span is closed.

{
  "event_type": "LLM_ERROR",
  "content": null,
  "attributes": {
    "root_agent_name": "my_bq_agent"
  },
  "error_message": "Error 429: Resource exhausted",
  "latency_ms": {
    "total_ms": 350
  }
}

Tool usage (plugin lifecycle)

These events track the execution of tools by the agent. Each tool event includes a tool_origin field that classifies the tool's provenance:

Tool Origin	Description
LOCAL	FunctionTool instances (local Python functions)
MCP	Model Context Protocol tools (McpTool instances)
SUB_AGENT	AgentTool instances (sub-agents)
A2A	Remote Agent-to-Agent instances (RemoteA2aAgent)
TRANSFER_AGENT	TransferToAgentTool instances
UNKNOWN	Unclassified tools

4. TOOL_STARTING

Logged when an agent begins executing a tool.

{
  "event_type": "TOOL_STARTING",
  "content": {
    "tool": "list_dataset_ids",
    "args": {
      "project_id": "bigquery-public-data"
    },
    "tool_origin": "LOCAL"
  }
}

5. TOOL_COMPLETED

Logged when a tool execution finishes.

{
  "event_type": "TOOL_COMPLETED",
  "content": {
    "tool": "list_dataset_ids",
    "result": [
      "austin_311",
      "austin_bikeshare"
    ],
    "tool_origin": "LOCAL"
  },
  "latency_ms": {
    "total_ms": 467
  }
}

6. TOOL_ERROR

Logged when a tool execution fails with an exception. Captures the tool name, arguments, tool origin, and error message.

{
  "event_type": "TOOL_ERROR",
  "content": {
    "tool": "list_dataset_ids",
    "args": {
      "project_id": "nonexistent-project"
    },
    "tool_origin": "LOCAL"
  },
  "error_message": "Error 404: Dataset not found",
  "latency_ms": {
    "total_ms": 150
  }
}

State Management

These events track changes to the agent's state, typically triggered by tools.

7. STATE_DELTA

Tracks changes to the agent's internal state (e.g., token cache updates).

{
  "event_type": "STATE_DELTA",
  "attributes": {
    "state_delta": {
      "bigquery_token_cache": "{\"token\": \"ya29...\", \"expiry\": \"...\"}"
    }
  }
}

Agent lifecycle & Generic Events

Event Type	Content (JSON) Structure
INVOCATION_STARTING	{}
INVOCATION_COMPLETED	{}
AGENT_STARTING	"You are a helpful agent..."
AGENT_COMPLETED	{}
USER_MESSAGE_RECEIVED	{"text_summary": "Help me book a flight."}

Human-in-the-Loop (HITL) Events

The plugin automatically detects calls to ADK's synthetic HITL tools and emits dedicated event types for them. These events are logged in addition to the normal TOOL_STARTING / TOOL_COMPLETED events.

The following HITL tool names are recognized:

• adk_request_credential — Request for user credentials (e.g., OAuth tokens)
• adk_request_confirmation — Request for user confirmation before proceeding
• adk_request_input — Request for free-form user input

Event Type	Trigger	Content (JSON) Structure
HITL_CREDENTIAL_REQUEST	Agent calls adk_request_credential	{"tool": "adk_request_credential", "args": {...}}
HITL_CREDENTIAL_REQUEST_COMPLETED	User provides credential response	{"tool": "adk_request_credential", "result": {...}}
HITL_CONFIRMATION_REQUEST	Agent calls adk_request_confirmation	{"tool": "adk_request_confirmation", "args": {...}}
HITL_CONFIRMATION_REQUEST_COMPLETED	User provides confirmation response	{"tool": "adk_request_confirmation", "result": {...}}
HITL_INPUT_REQUEST	Agent calls adk_request_input	{"tool": "adk_request_input", "args": {...}}
HITL_INPUT_REQUEST_COMPLETED	User provides input response	{"tool": "adk_request_input", "result": {...}}

HITL request events are detected from function_call parts in on_event_callback. HITL completion events are detected from function_response parts in both on_event_callback and on_user_message_callback.

GCS Offloading Examples (Multimodal & Large Text)

When gcs_bucket_name is configured, large text and multimodal content (images, audio, etc.) are automatically offloaded to GCS. The content column will contain a summary or placeholder, while content_parts contains the object_ref pointing to the GCS URI.

Offloaded Text Example

{
  "event_type": "LLM_REQUEST",
  "content_parts": [
    {
      "part_index": 1,
      "mime_type": "text/plain",
      "storage_mode": "GCS_REFERENCE",
      "text": "AAAA... [OFFLOADED]",
      "object_ref": {
        "uri": "gs://haiyuan-adk-debug-verification-1765319132/2025-12-10/e-f9545d6d/ae5235e6_p1.txt",
        "authorizer": "us.bqml_connection",
        "details": {"gcs_metadata": {"content_type": "text/plain"}}
      }
    }
  ]
}

Offloaded Image Example

{
  "event_type": "LLM_REQUEST",
  "content_parts": [
    {
      "part_index": 2,
      "mime_type": "image/png",
      "storage_mode": "GCS_REFERENCE",
      "text": "[MEDIA OFFLOADED]",
      "object_ref": {
        "uri": "gs://haiyuan-adk-debug-verification-1765319132/2025-12-10/e-f9545d6d/ae5235e6_p2.png",
        "authorizer": "us.bqml_connection",
        "details": {"gcs_metadata": {"content_type": "image/png"}}
      }
    }
  ]
}

Querying Offloaded Content (Get Signed URLs)

SELECT
  timestamp,
  event_type,
  part.mime_type,
  part.storage_mode,
  part.object_ref.uri AS gcs_uri,
  -- Generate a signed URL to read the content directly (requires connection_id configuration)
  STRING(OBJ.GET_ACCESS_URL(part.object_ref, 'r').access_urls.read_url) AS signed_url
FROM `your-gcp-project-id.your-dataset-id.agent_events`,
UNNEST(content_parts) AS part
WHERE part.storage_mode = 'GCS_REFERENCE'
ORDER BY timestamp DESC
LIMIT 10;

Advanced analysis queries

Trace a specific conversation turn using trace_id

SELECT timestamp, event_type, agent, JSON_VALUE(content, '$.response') as summary
FROM `your-gcp-project-id.your-dataset-id.agent_events`
WHERE trace_id = 'your-trace-id'
ORDER BY timestamp ASC;

Token usage analysis (accessing JSON fields)

SELECT
  AVG(CAST(JSON_VALUE(content, '$.usage.total') AS INT64)) as avg_tokens
FROM `your-gcp-project-id.your-dataset-id.agent_events`
WHERE event_type = 'LLM_RESPONSE';

Querying Multimodal Content (using content_parts and ObjectRef)

SELECT
  timestamp,
  part.mime_type,
  part.object_ref.uri as gcs_uri
FROM `your-gcp-project-id.your-dataset-id.agent_events`,
UNNEST(content_parts) as part
WHERE part.mime_type LIKE 'image/%'
ORDER BY timestamp DESC;

Analyze Multimodal Content with BigQuery Remote Model (Gemini)

SELECT
  logs.session_id,
  -- Get a signed URL for the image
  STRING(OBJ.GET_ACCESS_URL(parts.object_ref, "r").access_urls.read_url) as signed_url,
  -- Analyze the image using a remote model (e.g., gemini-pro-vision)
  AI.GENERATE(
    ('Describe this image briefly. What company logo?', parts.object_ref)
  ) AS generated_result
FROM
  `your-gcp-project-id.your-dataset-id.agent_events` logs,
  UNNEST(logs.content_parts) AS parts
WHERE
  parts.mime_type LIKE 'image/%'
ORDER BY logs.timestamp DESC
LIMIT 1;

Latency Analysis (LLM & Tools)

SELECT
  event_type,
  AVG(CAST(JSON_VALUE(latency_ms, '$.total_ms') AS INT64)) as avg_latency_ms
FROM `your-gcp-project-id.your-dataset-id.agent_events`
WHERE event_type IN ('LLM_RESPONSE', 'TOOL_COMPLETED')
GROUP BY event_type;

Span Hierarchy & Duration Analysis

SELECT
  span_id,
  parent_span_id,
  event_type,
  timestamp,
  -- Extract duration from latency_ms for completed operations
  CAST(JSON_VALUE(latency_ms, '$.total_ms') AS INT64) as duration_ms,
  -- Identify the specific tool or operation
  COALESCE(
    JSON_VALUE(content, '$.tool'),
    'LLM_CALL'
  ) as operation
FROM `your-gcp-project-id.your-dataset-id.agent_events`
WHERE trace_id = 'your-trace-id'
  AND event_type IN ('LLM_RESPONSE', 'TOOL_COMPLETED')
ORDER BY timestamp ASC;

Error Analysis (LLM & Tool Errors)

SELECT
  timestamp,
  event_type,
  agent,
  error_message,
  JSON_VALUE(content, '$.tool') as tool_name,
  CAST(JSON_VALUE(latency_ms, '$.total_ms') AS INT64) as latency_ms
FROM `your-gcp-project-id.your-dataset-id.agent_events`
WHERE event_type IN ('LLM_ERROR', 'TOOL_ERROR')
ORDER BY timestamp DESC
LIMIT 20;

Tool Provenance Analysis

SELECT
  JSON_VALUE(content, '$.tool_origin') as tool_origin,
  JSON_VALUE(content, '$.tool') as tool_name,
  COUNT(*) as call_count,
  AVG(CAST(JSON_VALUE(latency_ms, '$.total_ms') AS INT64)) as avg_latency_ms
FROM `your-gcp-project-id.your-dataset-id.agent_events`
WHERE event_type = 'TOOL_COMPLETED'
GROUP BY tool_origin, tool_name
ORDER BY call_count DESC;

HITL Interaction Analysis

SELECT
  timestamp,
  event_type,
  session_id,
  JSON_VALUE(content, '$.tool') as hitl_tool,
  content
FROM `your-gcp-project-id.your-dataset-id.agent_events`
WHERE event_type LIKE 'HITL_%'
ORDER BY timestamp DESC
LIMIT 20;

7. AI-Powered Root Cause Analysis (Agent Ops)

Automatically analyze failed sessions to determine the root cause of errors using BigQuery ML and Gemini.

DECLARE failed_session_id STRING;
-- Find a recent failed session
SET failed_session_id = (
    SELECT session_id
    FROM `your-gcp-project-id.your-dataset-id.agent_events`
    WHERE error_message IS NOT NULL
    ORDER BY timestamp DESC
    LIMIT 1
);

-- Reconstruct the full conversation context
WITH SessionContext AS (
    SELECT
        session_id,
        STRING_AGG(CONCAT(event_type, ': ', COALESCE(TO_JSON_STRING(content), '')), '\n' ORDER BY timestamp) as full_history
    FROM `your-gcp-project-id.your-dataset-id.agent_events`
    WHERE session_id = failed_session_id
    GROUP BY session_id
)
-- Ask Gemini to diagnose the issue
SELECT
    session_id,
    AI.GENERATE(
        ('Analyze this conversation log and explain the root cause of the failure. Log: ', full_history),
        connection_id => 'your-gcp-project-id.us.my-connection',
        endpoint => 'gemini-2.5-flash'
    ).result AS root_cause_explanation
FROM SessionContext;

Conversational Analytics in BigQuery

You can also use BigQuery Conversational Analytics to analyze your agent logs using natural language. Use this tool to answer questions like:

• "Show me the error rate over time"
• "What are the most common tool calls?"
• "Identify sessions with high token usage"

Looker Studio Dashboard

You can visualize your agent's performance using our pre-built Looker Studio Dashboard template.

To connect this dashboard to your own BigQuery table, use the following link format, replacing the placeholders with your specific project, dataset, and table IDs:

https://lookerstudio.google.com/reporting/create?c.reportId=f1c5b513-3095-44f8-90a2-54953d41b125&ds.ds3.connector=bigQuery&ds.ds3.type=TABLE&ds.ds3.projectId=<your-project-id>&ds.ds3.datasetId=<your-dataset-id>&ds.ds3.tableId=<your-table-id>

Feedback

We welcome your feedback on BigQuery Agent Analytics. If you have questions, suggestions, or encounter any issues, please reach out to the team at bqaa-feedback@google.com.

Additional resources

• BigQuery Storage Write API
• Introduction to Object Tables
• Interactive Demo Notebook