Hands-On Tutorial: Building an Autonomous Financial Audit Agent Team with Antigravity SDK + Google…

Hands-On Tutorial: Building an Autonomous Financial Audit Agent Team with Antigravity SDK + Google Cloud

A step-by-step guide to building, testing, and deploying a multi-agent financial reconciliation workflow using the Google Antigravity SDK and Google Cloud Platform.

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What you will build

In this tutorial, you will build an autonomous, multi-agent financial audit system capable of reconciling vendor transactions against PDF invoices. Instead of relying on a single large language model prompt, you will architect a team of specialized agents, each with scoped responsibilities, strict security boundaries, and distinct capabilities.

This agent team consists of:

• Audit Orchestrator  — The manager. It holds the high-level objective, delegates tasks to subagents, and manages the 4-phase workflow state machine (trigger, gather, reconcile, report).
• Data Researcher Agent  — A read-only specialist tasked with querying BigQuery for pending vendor transactions.
• Invoice Analyzer Agent  — A read-only specialist that locates and extracts structured line-item data from PDF invoices stored in Google Cloud Storage (GCS).
• Reconciliation Engine Agent  — The analytical core. It receives data from the Researcher and Analyzer, matches transactions to invoices, verifies tax calculations, and flags mismatches.
• Human Compliance Gate  — Not an AI, but a declarative policy hook that pauses execution and escalates any discrepancy above $1,000 to a human compliance officer for manual review before writing results.

Why not just give one agent access to everything? Because a single agent with BigQuery write access, GCS access, and command execution creates an unacceptable blast radius. By organizing the workflow as a multi-agent team, we implement the principle of least privilege, isolate the complex reasoning, and create a verifiable chain of custody for every decision.

This tutorial uses a fully cloud-native approach with real Google Cloud services: BigQuery, Cloud Storage, Cloud Logging, and Cloud Trace. You will provision real infrastructure and validate results against live data.

Why the Google Antigravity SDK?

Building a production-grade multi-agent system from raw LLM API calls means solving a long list of infrastructure problems yourself: agent lifecycle management, tool registration and schema generation, inter-agent communication, policy enforcement, observability hooks, and safe concurrency, before you write a single line of business logic.

The Google Antigravity SDK (google-antigravity) is a Python framework purpose-built for exactly this. It provides the primitives that let you focus on what your agents should do rather than how to wire them together:

The SDK’s design philosophy is separation of concerns. Your agent’s reasoning (system instructions + tools) is defined independently from its governance (policies + hooks). This means you can swap from a permissive development policy to a locked-down production policy without changing a single line of agent code, a pattern you’ll see throughout this tutorial.

In this tutorial, you’ll use the SDK to build a team of four agents that collectively perform a financial reconciliation audit. Each agent gets its own scoped configuration, tools, and policies. The Orchestrator spawns specialist sub-agents, the policy engine enforces least-privilege access, and the hook system generates a complete compliance audit trail to Cloud Logging and Cloud Trace, all with the SDK handling the infrastructure plumbing.

Prerequisites

• A Google Cloud project with billing enabled should have Vertex AI API enabled (or a Gemini API key)
• gcloud CLI installed and authenticated
• Python 3.11+ with pip. We will be using google-cloud-bigquery Python client library.
• The Antigravity SDK installed (pip install google-antigravity)
• Basic familiarity with Google Cloud BigQuery SQL and Python

Get the Code and Prepare Sample Data

The complete source code for this tutorial is available on GitHub. Start by cloning the repository, installing dependencies, and generating the sample invoice PDFs that the agent will process:

# 1. Clone the repository
git clone https://github.com/rominirani/financial-audit-agent-tutorial.git
cd financial-audit-agent-tutorial

# 2. Create a virtual environment and install dependencies
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

# 3. Generate sample invoice PDFs
python scripts/generate_sample_invoices.py

You should see:

✅ Generated 4 sample invoice PDFs in /path/to/data/invoices

⚠️ Important _ : Set your_ Project ID before running any code. The codebase reads PROJECT_ID from the environment variable. Set it once in your terminal session: bash export PROJECT_ID="your-gcp-project-id". This value is used for BigQuery queries, Cloud Storage bucket naming ($PROJECT_ID-audit-invoices) and audit result writes. You will also pass -- project-id=$PROJECT_ID when running __main.py.

The script generates 4 invoice PDFs with deliberately planted discrepancies between what the vendor’s invoice says and what the ERP system (BigQuery) recorded. This is what the agent must discover:

The repository structure:

├── main.py # Entry point 
├── requirements.txt # Python dependencies
├── agents/ # Agent configurations 
│ ├── orchestrator.py
│ ├── data_researcher.py
│ ├── invoice_analyzer.py
│ └── reconciler.py
├── tools/ # Custom tools — BigQuery + GCS 
│ └── bigquery_tools.py
├── hooks/ # Observability hooks 
│ └── observability.py
├── policies/ # Safety policies 
│ └── audit_policies.py
├── scripts/ # Data generation
│ └── generate_sample_invoices.py
└── eval/ # Evaluation 
    ├── eval_dataset.jsonl
    └── run_eval.py

We will assume that you have cloned the repository first and use this tutorial as a guided walkthrough of the code. You’ll need to provision the Google Cloud infrastructure in the upcoming sections before running the agent.

GCP Services that we shall use

The enterprise architecture relies on several Google Cloud services to provide data, storage, and observability.

Architecture

Understanding the architecture is crucial before writing code. We separate concerns between the Agent Runtime, the Data Layer, and Human Governance.

The System Architecture diagrams showing the various agents and the Google Cloud services that they will interact with.

What does the workflow actually look like? Take a look below.

What about Security when it comes to the data flow?

This diagram maps two layers of security. The SDK Policy Layer controls what each agent can do at the tool level. The GCP IAM Layer controls what the service account can access at the infrastructure level. Both must allow an action for it to succeed.

Let’s understand which Agent can do what with each GCP service, via the table below:

Notice the defense in depth. Even if the Data Researcher’s LLM were prompt-injected to attempt a DELETE FROM vendor_transactions, it would be blocked at two levels: the SDK policy engine rejects non-SELECT queries, and the BigQuery IAM role only grants dataEditor on the financial_audit dataset (not bigquery.admin). The Orchestrator itself has no direct data access, it can only delegate to scoped subagents.

The data flow is strictly controlled. The Orchestrator manages state and escalation but does not touch raw data. The Researcher and Analyzer are read-only. The Reconciler can only write to specific output locations.

This illustrates the principle of least privilege applied to AI. Each agent’s policy represents the MINIMUM set of permissions required for its specific job. The Researcher doesn’t need to read PDFs, so it doesn’t get GCS access. The Analyzer doesn’t need database access, so BigQuery is denied. This compartmentalization ensures that even if an agent hallucinates or is subjected to prompt injection via a malicious invoice PDF, it cannot compromise the broader system.

Step-by-Step GCP Provisioning

Create Project & Enable APIs

Start by creating a dedicated GCP project and enabling the six cloud services your agent team will use. Each API corresponds to a specific role: BigQuery for data, Cloud Storage for PDFs, Cloud Logging and Cloud Trace for observability, and Cloud Run for future deployment.

export PROJECT_ID="financial-audit-tutorial"
gcloud projects create $PROJECT_ID --name="Financial Audit Tutorial"
gcloud config set project $PROJECT_ID
gcloud services enable bigquery.googleapis.com storage.googleapis.com logging.googleapis.com cloudtrace.googleapis.com secretmanager.googleapis.com run.googleapis.com

Create BigQuery Dataset & Tables

We only create two tables: vendor_transactions (the ERP source of truth) and audit_results (where the agent writes findings). Invoice data is not pre-loaded into BigQuery, the agent extracts it from PDF files stored in GCS.

bq mk --dataset $PROJECT_ID:financial_audit

# Create vendor_transactions — the ERP source of truth
bq mk --table $PROJECT_ID:financial_audit.vendor_transactions \
vendor_id:STRING,vendor_name:STRING,invoice_num:STRING,amount:FLOAT64,currency:STRING,tax_rate:FLOAT64,status:STRING,quarter:STRING,transaction_date:DATE

# Create audit_results — the agent writes its findings here
bq mk --table $PROJECT_ID:financial_audit.audit_results \
execution_id:STRING,vendor_id:STRING,invoice_num:STRING,transaction_amount:FLOAT64,invoice_amount:FLOAT64,discrepancy_amount:FLOAT64,status:STRING,agent_notes:STRING,reviewed_by:STRING,timestamp:TIMESTAMP

Note: In the following SQL script, we are intentionally planting specific discrepancies in the data to test our agent team’s analytical capabilities:

• Vendor 8492 : The transaction records an 8.5% tax rate, but the invoice was calculated at 6.25%.
• Vendor 3301 : The transaction is recorded in USD, but the invoice is in EUR (with the exact same numeric amount, testing currency awareness).
• Vendor 5567 : There is a duplicate invoice number with two different amounts ($23,400 vs $24,100), testing deduplication logic.

Populate the transactions table with ERP data. These represent what your company’s accounting system has recorded — amounts, tax rates, currencies, and invoice numbers. Think of this as the “source of truth” from the internal financial system:

-- Run this in the BigQuery Console or via bq query
INSERT INTO `YOUR_PROJECT_ID.financial_audit.vendor_transactions` 
(vendor_id, vendor_name, invoice_num, amount, currency, tax_rate, status, quarter, transaction_date) VALUES
('8492', 'TechCorp Solutions', 'INV-8492-Q3-001', 142300.00, 'USD', 0.085, 'PENDING', 'Q3', '2026-07-15'),
('1022', 'OfficeSupplies Co', 'INV-1022-Q3-014', 4500.00, 'USD', 0.05, 'PENDING', 'Q3', '2026-07-20'),
('3301', 'Global Services Ltd', 'INV-3301-Q3-099', 87500.00, 'USD', 0.10, 'PENDING', 'Q3', '2026-08-01'),
('5567', 'Consulting Group Inc', 'INV-5567-Q3-001', 23400.00, 'USD', 0.0, 'PENDING', 'Q3', '2026-08-10'),
('5567', 'Consulting Group Inc', 'INV-5567-Q3-001', 24100.00, 'USD', 0.0, 'PENDING', 'Q3', '2026-08-12');

Notice we are not inserting any invoice data into BigQuery. The corresponding invoice PDFs will be uploaded to GCS. The agent must read and parse those PDFs to get the vendor’s side of the story, then compare against these transaction records. The discrepancies between these two data sources are what the agent needs to discover autonomously.

Create GCS Bucket and Upload Invoices

If you followed the instructions earlier, you already have 4 sample invoice PDFs in data/invoices/. Now create a Cloud Storage bucket and upload them. The -l us-central1 flag co-locates the bucket with your BigQuery dataset for low-latency access:

# Create a regional bucket for invoice PDFs
gsutil mb -l us-central1 gs://$PROJECT_ID-audit-invoices

# Upload all generated invoices into a Q3/ prefix
gsutil -m cp data/invoices/*.pdf gs://$PROJECT_ID-audit-invoices/Q3/

# Verify uploads — you should see 4 PDF files
gsutil ls gs://$PROJECT_ID-audit-invoices/Q3/

The Q3/ prefix organizes invoices by quarter. The agent’s list_invoices_in_gcs() tool filters by this prefix when searching for invoices to reconcile.

Create Service Account with Least-Privilege IAM

Create a dedicated service account for the audit agent and grant it only the minimum IAM roles required. Each role maps to a specific capability: dataViewer for reading BigQuery tables, jobUser for running queries, logWriter for writing structured audit logs, cloudtrace.agent for publishing trace spans, and storage.objectViewer for reading invoice PDFs from GCS. The agent cannot delete data, modify tables, or access other projects.

# Create the service account
gcloud iam service-accounts create audit-agent-sa \
    --display-name="Audit Agent Service Account"
SA_EMAIL="audit-agent-sa@$PROJECT_ID.iam.gserviceaccount.com"
# BigQuery: read transactions + write audit results
gcloud projects add-iam-policy-binding $PROJECT_ID \
    --member="serviceAccount:$SA_EMAIL" \
    --role="roles/bigquery.dataViewer" --condition=None
gcloud projects add-iam-policy-binding $PROJECT_ID \
    --member="serviceAccount:$SA_EMAIL" \
    --role="roles/bigquery.jobUser" --condition=None
# Cloud Logging: write structured audit events
gcloud projects add-iam-policy-binding $PROJECT_ID \
    --member="serviceAccount:$SA_EMAIL" \
    --role="roles/logging.logWriter" --condition=None
# Cloud Trace: publish distributed trace spans
gcloud projects add-iam-policy-binding $PROJECT_ID \
    --member="serviceAccount:$SA_EMAIL" \
    --role="roles/cloudtrace.agent" --condition=None
# Cloud Storage: read-only access to invoice PDFs
gcloud projects add-iam-policy-binding $PROJECT_ID \
    --member="serviceAccount:$SA_EMAIL" \
    --role="roles/storage.objectViewer" --condition=None

The -- condition=None flag is required when your project already has conditional IAM policies. Without it, the command will fail with a ”Adding a binding without specifying a condition is prohibited” error.

Verify Setup

Before writing any code, verify that all infrastructure was provisioned correctly. These two commands confirm the BigQuery dataset exists and the service account has the expected IAM roles:

# Verify BigQuery dataset and tables exist (audit_results & vendor_transactions)
bq ls $PROJECT_ID:financial_audit
# Verify IAM roles assigned to the service account
gcloud projects get-iam-policy $PROJECT_ID \
  --flatten="bindings[].members" \
  --format='table(bindings.role)' \
  --filter="bindings.members:audit-agent-sa"

You should see roles/bigquery.dataViewer, roles/bigquery.jobUser, roles/logging.logWriter, roles/cloudtrace.agent, and roles/storage.objectViewer in the output.

Antigravity SDK Code — Step by Step

Let’s first start with setting up a project folder structure and necessary environment in place.

Project Scaffolding

Clone the repository, set up a Python virtual environment, and install the dependencies. google-antigravity is the SDK itself; google-cloud-bigquery and google-cloud-storage provide the GCP client libraries your tools will wrap; reportlab generates sample invoice PDFs; and PyPDF2 enables PDF text extraction at runtime:

git clone https://github.com/rominirani/financial-audit-agent-tutorial.git
cd financial-audit-agent-tutorial
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

Define Custom Tools

Agents need tools to interact with external systems. In this tutorial, we define custom Python functions that wrap the Google Cloud client libraries (google-cloud-bigquery and google-cloud-storage) and pass them to the agent via the tools parameter in LocalAgentConfig.

Each tool is a standard Python function with type hints and a descriptive docstring. The SDK automatically registers them as callable tools the agent can invoke. The agent sees a list of available functions with descriptions and calls them as needed.

Implement the Tools

(tools/bigquery_tools.py)

This file contains all four tool functions. Each function wraps a Google Cloud client library call and returns a JSON string that the agent can parse and reason over.

See the complete implementation with all four tools: [bigquery_tools.py]

from google.cloud import bigquery, storage
import json
import os

PROJECT_ID = os.environ.get("PROJECT_ID", "YOUR_PROJECT_ID")
DATASET = "financial_audit"
BUCKET_NAME = f"{PROJECT_ID}-audit-invoices"

_client = bigquery.Client(project=PROJECT_ID)

def query_vendor_transactions(quarter: str = "Q3") -> str:
    """Query BigQuery for all PENDING vendor transactions in a given quarter."""
    query = f"""
    SELECT vendor_id, vendor_name, invoice_num, amount, currency, tax_rate, status, quarter, transaction_date
    FROM `{PROJECT_ID}.{DATASET}.vendor_transactions`
    WHERE status = 'PENDING' AND quarter = '{quarter}'
    ORDER BY vendor_id, invoice_num
    """
    rows = _client.query(query).result()
    results = [{"vendor_id": row.vendor_id, "amount": row.amount, ...} for row in rows]
    return json.dumps({"total_transactions": len(results), "transactions": results}, indent=2)

def list_invoices_in_gcs(bucket_name: str, prefix: str = "Q3/") -> str:
    """List all invoice PDF files in a Google Cloud Storage bucket."""
    ...

def read_invoice_from_gcs(bucket_name: str, blob_name: str) -> str:
    """Read and extract structured data from an invoice PDF stored in GCS."""
    ...

def write_audit_result(vendor_id: str, invoice_num: str, status: str, ...) -> str:
    """Write an audit reconciliation result to the BigQuery audit_results table."""
    ...

AUDIT_TOOLS = [query_vendor_transactions, list_invoices_in_gcs, read_invoice_from_gcs, write_audit_result]

These four functions give the agent typed, documented tools with clear input/output contracts. The read_invoice_from_gcs tool downloads the PDF, extracts text using PyPDF2, and parses structured fields (amounts, tax rates, currency) via regex. The agent never sees raw PDF bytes, it receives clean JSON.

Install PyPDF2 for PDF text extraction: pip install PyPDF2. Each tool function’s docstring becomes the tool’s description visible to the LLM. Write clear, specific docstrings, they directly affect the agent’s ability to choose the right tool.

Define Safety Policies

(policies/audit_policies.py)

Safety policies are the SDK’s mechanism for controlling what agents can and cannot do. Each policy tier represents a different level of trust. The key difference: staging requires a human at the terminal to approve writes, while production runs fully autonomously with argument-level validation. The compliance_officer_approval_handler simulates a human gate , in a real deployment, you'd replace the input() call with a Slack notification or approval workflow:

from google.antigravity.hooks import policy

VALID_STATUSES = {"MATCHED", "DISCREPANCY", "ESCALATED", "UNMATCHED"}

async def compliance_officer_approval_handler(tool_call) -> bool:
    """Escalation handler for high-risk actions in staging.

    Receives a types.ToolCall object. Use tool_call.name for the tool name
    and tool_call.args for the arguments dict.
    """
    print(f"\n🚨 ESCALATION REQUIRED 🚨")
    print(f"Action requested: {tool_call.name}")
    print(f"Arguments: {tool_call.args}")

    # In a real environment, this would ping Slack/Email and wait.
    # For this tutorial, we simulate a prompt.
    response = input("Compliance Officer, approve this action? (y/n): ")
    return response.lower() == 'y'

# --- Development: full permissions, no restrictions ---
DEVELOPMENT_POLICIES = [
    policy.allow_all(),
]

# --- Staging: reads auto-allowed, writes require human approval ---
# Use this when testing interactively. You sit at the terminal and
# approve/reject each write_audit_result call before it hits BigQuery.
STAGING_POLICIES = [
    policy.deny_all(),
    # Read-only tools — allowed without approval
    policy.allow("query_vendor_transactions"),
    policy.allow("list_invoices_in_gcs"),
    policy.allow("read_invoice_from_gcs"),
    # Write tools — human must approve each one
    policy.ask_user("write_audit_result", handler=compliance_officer_approval_handler),
]

# --- Production: fully autonomous, no human prompts ---
# Use this for unattended execution (Cloud Run, cron jobs).
# Writes are auto-allowed but ONLY if the status is valid.
# No ask_user — there's no human at the terminal in production.
PRODUCTION_POLICIES = [
    policy.deny_all(),
    # Read-only tools — allowed
    policy.allow("query_vendor_transactions"),
    policy.allow("list_invoices_in_gcs"),
    policy.allow("read_invoice_from_gcs"),
    # Write tools — auto-allowed but only with valid status values
    policy.allow("write_audit_result",
                 when=lambda args: args.get("status", "") in VALID_STATUSES,
                 name="allow_valid_audit_writes"),
]

Notice the three distinct policy tiers and the critical difference between staging and production:

• DEVELOPMENT_POLICIES : Uses allow_all(), meaning the agent can do anything. Use this only on your local machine for rapid iteration and debugging.
• STAGING_POLICIES : Reads are auto-allowed, but write_audit_result triggers the compliance_officer_approval_handler which pauses the agent and prompts you via stdin. You review the vendor ID, status, and amounts before approving each write.
• PRODUCTION_POLICIES : Designed for unattended execution on Cloud Run or cron. There’s no ask_user (nobody is at the terminal). Instead, writes are auto-allowed but only if the status argument is a valid value (MATCHED, DISCREPANCY, ESCALATED, or UNMATCHED). Any hallucinated or malformed status is silently denied.

Policy tool names must match the registered function names and not generic categories. Since our tools are query_vendor_transactions, list_invoices_in_gcs, read_invoice_from_gcs, and write_audit_result, those are the exact strings used in policy.allow() and policy.ask_user(). Using wrong names (e.g., bigquery_query) would cause a silent mismatch and the tool would be denied by deny_all().

Define the Agent Team

Each agent in the team gets its own configuration file defining its system instructions (the “job description”), permitted tools, and security policies. The Antigravity SDK uses LocalAgentConfig for subagents and CapabilitiesConfig to control whether an agent can spawn other agents.

agents/orchestrator.py

This is the lead auditor. It holds the high-level workflow, has access to all tools, and can spawn subagents. Notice how the system instructions are highly directive — they include the concrete bucket name, exact tool call syntax for each step, and explicit instructions to not stop partway through:

from google.antigravity import LocalAgentConfig, CapabilitiesConfig
from tools.bigquery_tools import AUDIT_TOOLS, BUCKET_NAME

def get_orchestrator_config(policies, workspace, project_id=None, quarter="Q3"):
    return LocalAgentConfig(
        system_instructions=f"""
        You are the Lead Financial Auditor. You MUST complete the entire reconciliation
        workflow in a single session. Do NOT stop partway through.

        GCS BUCKET: {BUCKET_NAME}
        QUARTER: {quarter}

        WORKFLOW — execute ALL steps before producing your final report:

        STEP 1: Call query_vendor_transactions(quarter="{quarter}") to get all PENDING
                transactions from BigQuery.

        STEP 2: Call list_invoices_in_gcs() to list all invoice PDFs in the GCS bucket.

        STEP 3: For EACH invoice PDF returned in Step 2, call read_invoice_from_gcs()
                with the invoice_path (e.g. "Q3/INV-8492-Q3-001.pdf").
                Extract: vendor_id, invoice_num, base_amount, tax_rate, total_amount, currency.

        STEP 4: RECONCILE each transaction (Step 1) against its corresponding invoice (Step 3).
                Match by vendor_id and invoice_num. For each pair:
                - Compare transaction amount vs invoice total_amount (tolerance: $0.01)
                - Verify tax_rate matches
                - Check currency consistency
                - Classify as MATCHED, DISCREPANCY, or UNMATCHED

        STEP 5: For each reconciled pair, call write_audit_result() to record the finding.
                If any discrepancy exceeds $1,000, set status to ESCALATED.

        STEP 6: Produce a FINAL COMPLIANCE REPORT summarizing:
                - Total vendors audited
                - Number of matches vs discrepancies
                - Details of each discrepancy (vendor, amount difference, cause)
                - Escalation recommendations

        CRITICAL RULES:
        - You MUST call read_invoice_from_gcs() for EVERY invoice — do not skip any
        - Never modify the vendor_transactions table directly
        - Always escalate discrepancies over $1,000 — do not auto-approve
        - Log every decision with a clear rationale
        - Do NOT stop after listing invoices — you must read, reconcile, and report
        """,
        tools=AUDIT_TOOLS,
        capabilities=CapabilitiesConfig(enable_subagents=True),
        model="gemini-2.5-flash",
        policies=policies,
        workspaces=[workspace],
        vertex=True if project_id else None,
        project=project_id,
        location="us-central1" if project_id else None,
    )

What’s happening here:

1. model=”gemini-2.5-flash” : Explicitly sets the model. Always verify available models with your Vertex AI project.
2. tools=AUDIT_TOOLS:Passes the custom BigQuery functions as callable tools.
3. vertex=True : Routes API calls through Vertex AI using Application Default Credentials (ADC).
4. project and location:Specify the GCP project and region for Vertex AI.
5. CapabilitiesConfig(enable_subagents=True) : Authorizes this agent to dynamically spawn child agents.

agents/data_researcher.py

The BigQuery specialist. It has a single allowed operation: run SELECT queries. The policy uses a when lambda to inspect the query string at runtime, ensuring only SELECT statements pass. Any INSERT, UPDATE, or DELETE attempt is denied before it reaches BigQuery:

from google.antigravity import LocalAgentConfig
from google.antigravity.hooks import policy

def get_data_researcher_config(workspace):
    return LocalAgentConfig(
        system_instructions="""
        You are a Data Research Specialist. Your job is to query BigQuery
        for vendor transaction records. You have READ-ONLY access.

        Return results as a structured JSON summary with:
        - Total transactions found
        - List of vendor_id, invoice_num, amount, currency, tax_rate
        - Any data quality issues noted
        """,
        policies=[
            policy.deny_all(),
            policy.allow("bigquery_query",
                         when=lambda args: args.get("Query", "").strip().upper().startswith("SELECT"),
                         name="allow_bq_select_only"),
        ],
        workspaces=[workspace],
    )

agents/invoice_analyzer.py

The PDF extraction specialist. It can list and read files but has zero BigQuery access. Its system instructions tell it to enumerate GCS PDFs and extract structured data from each one. Even if this agent were compromised via a malicious PDF, it cannot touch the database:

from google.antigravity import LocalAgentConfig
from google.antigravity.hooks import policy

def get_invoice_analyzer_config(workspace):
    return LocalAgentConfig(
        system_instructions="""
        You are an Invoice Analysis Specialist. Your job is to:
        1. List all invoice PDFs in the GCS bucket using list_invoices_in_gcs()
        2. Read each PDF using read_invoice_from_gcs() to extract structured data

        For each invoice, extract and return:
        - Vendor name and ID
        - Invoice number
        - Base amount, tax rate, tax amount
        - Total invoice amount
        - Currency

        Return results as structured JSON.
        """,
        policies=[
            policy.deny_all(),
            policy.allow("view_file"),
            policy.allow("list_dir"),
        ],
        workspaces=[workspace],
    )

agents/reconciler.py

The analytical core. It receives pre-fetched data from the other agents and performs the comparison logic. Its policies restrict it to view_file only — it cannot query databases or access GCS directly. The Orchestrator passes it the data it needs:

from google.antigravity import LocalAgentConfig
from google.antigravity.hooks import policy

def get_reconciler_config(workspace):
    return LocalAgentConfig(
        system_instructions="""
        You are a Reconciliation Engine. You receive two datasets:
        1. Transaction records from BigQuery
        2. Invoice data extracted from PDFs

        For each transaction-invoice pair:
        - Match by vendor_id and invoice_num
        - Compare transaction amount vs invoice amount (tolerance: $0.01)
        - Verify tax rate calculations
        - Check currency consistency

        Classify each pair as:
        - MATCHED: amounts match within tolerance
        - DISCREPANCY: amounts differ — include the difference and likely cause
        - UNMATCHED: transaction exists but no corresponding invoice found

        Flag any discrepancy exceeding $1,000 for human escalation.
        """,
        policies=[
            policy.deny_all(),
            policy.allow("view_file"),
        ],
        workspaces=[workspace],
    )

Notice the progression of trust across the team. The Orchestrator has broad administrative authority to spawn subagents and orchestrate the workflow but doesn’t directly touch the raw data. As we move to the Researcher, Analyzer, and Reconciler, the permissions narrow significantly. They are granted only the precise read or write access required for their specific analytical tasks, encapsulating the complex reasoning in isolated, secure sandboxes.

Implement Observability Hooks

(hooks/observability.py)

Hooks are the SDK’s mechanism for injecting cross-cutting concerns, logging, tracing, alerting into the agent lifecycle without modifying the agent’s core logic. We implement three hooks that integrate with Cloud Logging and Cloud Trace for production observability, while also printing to the console for local development.

See the full implementation with Cloud Logging/Trace setup and graceful degradation: [observability.py]

from google.antigravity.hooks import on_session_start, post_tool_call, on_session_end
from google.antigravity import types
from opentelemetry import trace
# ... Cloud Logging and Cloud Trace setup (see full file for details) ...

AGENT_NAME = "audit-orchestrator"
_active_span = None
_tool_call_count = 0

@on_session_start
async def audit_session_start():
    global _active_span, _tool_call_count
    _tool_call_count = 0
    _log("INFO", "Audit session started", event="SESSION_START", agent=AGENT_NAME)

    if CLOUD_TRACE_ENABLED:
        _active_span = _tracer.start_span("financial-audit-session")
        _active_span.set_attribute("audit.type", "vendor-reconciliation")
        _active_span.set_attribute("agent.name", AGENT_NAME)

@post_tool_call
async def audit_tool_invocation(data: types.ToolResult):
    """Create a child span explicitly parented under the session span."""
    global _tool_call_count
    _tool_call_count += 1
    tool_name = data.name if hasattr(data, 'name') else str(data)

    _log("INFO", f"Tool invoked: {tool_name}",
         event="TOOL_INVOCATION", agent=AGENT_NAME, tool=tool_name)

    if CLOUD_TRACE_ENABLED and _tracer and _active_span:
        # Key: set_span_in_context() parents tool spans under session span
        parent_ctx = trace.set_span_in_context(_active_span)
        tool_span = _tracer.start_span(f"tool:{tool_name}", context=parent_ctx)
        tool_span.set_attribute("tool.name", tool_name)
        tool_span.set_attribute("tool.call_index", _tool_call_count)
        tool_span.end()

# @on_session_end closes the session span and logs total_tool_calls

AUDIT_HOOKS = [audit_session_start, audit_tool_invocation, audit_session_end]

What each hook does:

• @on_session_start — Fires when the agent session begins. Creates a Cloud Trace span and logs the start event to Cloud Logging.
• @post_tool_call — Fires after every tool invocation. Receives a types.ToolResult object. Logs the tool name and agent ID to Cloud Logging and creates a child span in Cloud Trace. This is the core of your compliance audit trail.
• @on_session_end — Fires when the session ends. Closes the trace span and logs the completion event.

The hooks use graceful degradation — if google-cloud-logging or opentelemetry aren’t installed, they fall back to console-only output. This means the same code works in both local development and production without conditional imports.

To enable Cloud Logging and Cloud Trace, install the additional dependencies:

pip install google-cloud-logging opentelemetry-api opentelemetry-sdk opentelemetry-exporter-gcp-trace

Generate Sample Invoice PDFs

(scripts/generate_sample_invoices.py)

If you already ran this script and uploaded the PDFs in an earlier step, you can skip ahead to the next section. This section explains the code for reference.

This script uses the reportlab library to generate realistic-looking invoice PDFs with structured text fields. Each PDF contains the invoice number, vendor name and ID, base amount, tax rate, tax amount, and total, formatted so that our read_invoice_from_gcs() tool can parse them via regex.

The key design decision is the planted discrepancies between these invoices and the BigQuery transaction records. This is what gives the agent something meaningful to discover:

from reportlab.pdfgen import canvas

def create_invoice(vendor_id, vendor_name, inv_num, amount, currency, tax_rate, output_dir):
    """Generate a single invoice PDF with structured text fields."""
    ...

# Normal — matches ERP exactly
create_invoice('1022', 'OfficeSupplies Co', 'INV-1022-Q3-014', 4500.00, 'USD', 0.05, '../data/invoices')
# Planted discrepancy 1: Tax calculation error (invoice shows 6.25% instead of 8.5%)
create_invoice('8492', 'TechCorp', 'INV-8492-Q3-001', 138750.00, 'USD', 0.0625, '../data/invoices')
# Planted discrepancy 2: Currency mismatch (invoice in EUR, ERP in USD)
create_invoice('3301', 'Global Services', 'INV-3301-Q3-099', 87500.00, 'EUR', 0.10, '../data/invoices')
# Planted discrepancy 3: Duplicate (one PDF, two ERP records at different amounts)
create_invoice('5567', 'Consulting Group', 'INV-5567-Q3-001', 23400.00, 'USD', 0.0, '../data/invoices')

Write the Main Orchestration Script

(main.py)

This is the entry point that ties everything together. It parses command-line arguments to select the policy tier (dev/staging/prod), loads the corresponding policy set, configures the Orchestrator agent with Vertex AI credentials and observability hooks, and sends the initial audit prompt. The async with Agent(config) context manager handles the agent lifecycle (connection, execution, cleanup):

import asyncio
import argparse
import os
from google.antigravity import Agent
from agents.orchestrator import get_orchestrator_config
from policies.audit_policies import DEVELOPMENT_POLICIES, STAGING_POLICIES, PRODUCTION_POLICIES
from hooks.observability import AUDIT_HOOKS

async def main():
    parser = argparse.ArgumentParser(description="Financial Audit Agent Team")
    parser.add_argument("--mode", choices=["dev", "staging", "prod"], default="dev")
    parser.add_argument("--quarter", default="Q3")
    parser.add_argument("--project-id", default=None, help="GCP project ID for Vertex AI")
    args = parser.parse_args()

    # Select policy set
    policies = {
        "dev": DEVELOPMENT_POLICIES,
        "staging": STAGING_POLICIES,
        "prod": PRODUCTION_POLICIES,
    }[args.mode]

    workspace_dir = os.path.abspath(os.path.dirname( __file__ ))

    # Build orchestrator config
    config = get_orchestrator_config(
        policies=policies,
        workspace=workspace_dir,
        project_id=args.project_id,
        quarter=args.quarter,
    )
    config.hooks = AUDIT_HOOKS

    print(f"🚀 Starting Financial Audit — Mode: {args.mode}, Quarter: {args.quarter}")
    print(f"📋 Policies: {len(policies)} rules loaded")
    print(f"🔗 Vertex AI: {args.project_id}\n")

    async with Agent(config) as agent:
        response = await agent.chat(
            f"Execute the full {args.quarter} vendor invoice reconciliation now. "
            f"Complete ALL steps: query transactions, list invoices, read EVERY invoice PDF, "
            f"reconcile each transaction against its invoice, write audit results, "
            f"and produce the final compliance report. Do not stop until the report is complete."
        )

        # Print the agent's response
        print("\n" + "=" * 60)
        print("📊 AUDIT RESULTS")
        print("=" * 60)
        print(await response.text())

        # Token usage summary
        usage = agent.conversation.total_usage
        print(f"\n💰 Token Usage Summary:")
        print(f" Prompt tokens: {usage.prompt_token_count}")
        print(f" Output tokens: {usage.candidates_token_count}")
        print(f" Thinking tokens: {usage.thoughts_token_count}")
        print(f" Total tokens: {usage.total_token_count}")

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

The --project-id flag specifies the GCP project for Vertex AI. In CI/CD pipelines, set this via environment variables. The config.hooks = AUDIT_HOOKS line enables observability for the entire agent hierarchy.

Evaluate the Agent

I state upfront that my knowledge of Agent Evals is rudimentary at this point of time. What you will see is a code centric way in which I am evaluating the Agent Responses against a set of expected outputs. I know I can do better at that, but maybe in a future set of articles.

Evaluations let you systematically test the agent against known scenarios to catch regressions. You’ll create two files: a dataset of test cases and a runner script.

Create the Evaluation Dataset

eval/eval_dataset.jsonl

Each line is a JSON object with three fields: input (the prompt to send), expected_outcome (keywords the response must contain), and expected_tools (which tools should be invoked).

Create this file:

{"input": "Reconcile vendor 8492 for Q3.", "expected_outcome": "discrepancy, tax, escalat", "expected_tools": ["query_vendor_transactions", "list_invoices_in_gcs", "read_invoice_from_gcs"]}
{"input": "Reconcile vendor 1022 for Q3.", "expected_outcome": "match", "expected_tools": ["query_vendor_transactions", "list_invoices_in_gcs", "read_invoice_from_gcs"]}
{"input": "Reconcile vendor 3301 for Q3.", "expected_outcome": "currency, mismatch, EUR", "expected_tools": ["query_vendor_transactions", "list_invoices_in_gcs", "read_invoice_from_gcs"]}
{"input": "Reconcile vendor 5567 for Q3.", "expected_outcome": "duplicate, invoice", "expected_tools": ["query_vendor_transactions", "list_invoices_in_gcs", "read_invoice_from_gcs"]}

Create the Eval Runner Script

eval/run_eval.py

This script loads the dataset, sends each test case to the agent, and checks whether the expected keywords appear in the response. It reuses the same Agent API and orchestrator config from main.py:

See the full eval runner with CLI argument parsing: [run_eval.py]

async def run_eval(eval_file: str, project_id: str):
    """Run each eval case and check agent output against expected keywords."""
    with open(eval_file) as f:
        cases = [json.loads(line) for line in f if line.strip()]

    config = get_orchestrator_config(
        policies=DEVELOPMENT_POLICIES, workspace=workspace_dir, project_id=project_id,
    )
    config.hooks = AUDIT_HOOKS

    passed = 0
    async with Agent(config) as agent:
        for case in cases:
            response = await agent.chat(case["input"])
            result = await response.text()

            # Check if ALL expected keywords appear in the response
            keywords = [kw.strip().lower() for kw in case["expected_outcome"].split(",")]
            missing = [kw for kw in keywords if kw not in result.lower()]

            if not missing:
                print(f"✅ PASS")
                passed += 1
            else:
                print(f"❌ FAIL — missing keywords: {missing}")

Run the Evaluations

python eval/run_eval.py --project-id=$PROJECT_ID

Expected output:

📋 Loaded 4 eval cases from eval/eval_dataset.jsonl
--- Eval Case 1/4: Reconcile vendor 8492 for Q3. ---
✅ PASS
--- Eval Case 2/4: Reconcile vendor 1022 for Q3. ---
✅ PASS
--- Eval Case 3/4: Reconcile vendor 3301 for Q3. ---
✅ PASS
--- Eval Case 4/4: Reconcile vendor 5567 for Q3. ---
✅ PASS
============================================================
📊 EVAL RESULTS: 4/4 passed, 0/4 failed
============================================================

If a case fails, the runner prints which keywords were missing and a preview of the agent’s actual response, so you can debug the prompt:

--- Eval Case 1/4: Reconcile vendor 8492 for Q3. ---
❌ FAIL — missing keywords: ['escalat']
   Response preview: Found a tax rate discrepancy for vendor 8492. The ERP records 8.5% ...

Run evals after every prompt or tool change. The most common regression is the agent using different wording (e.g., flagged for review instead of escalated), which you can fix by adding more keyword variants to expected_outcome or by refining the system instructions.

Run the Agent

Before running, confirm all the prerequisites from earlier steps are in place:

• tools/bigquery_tools.py has your actual PROJECT_ID (not the placeholder)
• Sample invoice PDFs generated in data/invoices/
• BigQuery dataset financial_audit with both tables created and populated
• GCS bucket gs://$PROJECT_ID-audit-invoices with invoice PDFs uploaded to Q3/
• Service account created with least-privilege IAM roles
• Virtual environment activated with all dependencies installed

Run the agent with:

python main.py --mode=dev --quarter=Q3 --project-id=YOUR_PROJECT_ID

The command accepts three flags:

Try each policy tier to see how the SDK’s policy system changes agent behavior:

Development Mode

(--mode=dev)

Development mode uses allow_all() i.e. the agent has full, unrestricted permissions. This is ideal for debugging prompt logic and verifying the end-to-end workflow:

python main.py --mode=dev --quarter=Q3 --project-id=$PROJECT_ID

The agent runs without interruption, freely querying BigQuery, spawning subagents, reading PDFs from GCS, and writing results. Here is what a successful dev run looks like:

🚀 Starting Financial Audit — Mode: dev, Quarter: Q3
📋 Policies: 1 rules loaded
🔗 Vertex AI: gcp-experiments-349209

============================================================
📊 AUDIT RESULTS
============================================================

============================================================
🔍 FINANCIAL AUDIT SESSION STARTED — 2026-07-31T10:45:08.970628+00:00Z
============================================================

[AUDIT] {"timestamp": "2026-07-31T10:45:08.970644+00:00Z", "severity": "INFO", "message": "Audit session started", "event": "SESSION_START", "agent": "audit-orchestrator"}
[AUDIT] {"timestamp": "2026-07-31T10:45:21.650507+00:00Z", "severity": "INFO", "message": "Tool invoked: query_vendor_transactions", "event": "TOOL_INVOCATION", "agent": "audit-orchestrator", "tool": "query_vendor_transactions", "tool_call_index": 1}
[AUDIT] {"timestamp": "2026-07-31T10:45:26.589480+00:00Z", "severity": "INFO", "message": "Tool invoked: list_invoices_in_gcs", "event": "TOOL_INVOCATION", "agent": "audit-orchestrator", "tool": "list_invoices_in_gcs", "tool_call_index": 2}
[AUDIT] {"timestamp": "2026-07-31T10:45:39.201514+00:00Z", "severity": "INFO", "message": "Tool invoked: read_invoice_from_gcs", "event": "TOOL_INVOCATION", "agent": "audit-orchestrator", "tool": "read_invoice_from_gcs", "tool_call_index": 3}
[AUDIT] {"timestamp": "2026-07-31T10:45:46.483242+00:00Z", "severity": "INFO", "message": "Tool invoked: write_audit_result", "event": "TOOL_INVOCATION", "agent": "audit-orchestrator", "tool": "write_audit_result", "tool_call_index": 4}
[AUDIT] {"timestamp": "2026-07-31T10:45:51.549892+00:00Z", "severity": "INFO", "message": "Tool invoked: read_invoice_from_gcs", "event": "TOOL_INVOCATION", "agent": "audit-orchestrator", "tool": "read_invoice_from_gcs", "tool_call_index": 5}
[AUDIT] {"timestamp": "2026-07-31T10:45:57.574657+00:00Z", "severity": "INFO", "message": "Tool invoked: write_audit_result", "event": "TOOL_INVOCATION", "agent": "audit-orchestrator", "tool": "write_audit_result", "tool_call_index": 6}
[AUDIT] {"timestamp": "2026-07-31T10:46:02.324108+00:00Z", "severity": "INFO", "message": "Tool invoked: read_invoice_from_gcs", "event": "TOOL_INVOCATION", "agent": "audit-orchestrator", "tool": "read_invoice_from_gcs", "tool_call_index": 7}
[AUDIT] {"timestamp": "2026-07-31T10:46:09.536850+00:00Z", "severity": "INFO", "message": "Tool invoked: write_audit_result", "event": "TOOL_INVOCATION", "agent": "audit-orchestrator", "tool": "write_audit_result", "tool_call_index": 8}
[AUDIT] {"timestamp": "2026-07-31T10:46:14.693362+00:00Z", "severity": "INFO", "message": "Tool invoked: write_audit_result", "event": "TOOL_INVOCATION", "agent": "audit-orchestrator", "tool": "write_audit_result", "tool_call_index": 9}
[AUDIT] {"timestamp": "2026-07-31T10:46:19.938072+00:00Z", "severity": "INFO", "message": "Tool invoked: read_invoice_from_gcs", "event": "TOOL_INVOCATION", "agent": "audit-orchestrator", "tool": "read_invoice_from_gcs", "tool_call_index": 10}
[AUDIT] {"timestamp": "2026-07-31T10:46:26.439379+00:00Z", "severity": "INFO", "message": "Tool invoked: write_audit_result", "event": "TOOL_INVOCATION", "agent": "audit-orchestrator", "tool": "write_audit_result", "tool_call_index": 11}
## Q3 Vendor Invoice Reconciliation Compliance Report

This report details the reconciliation of all pending Q3 vendor transactions against their corresponding invoices in the GCS bucket.

---

### Audit Summary

* **Total Vendors Audited:** 4
* **Total Reconciliations Performed:** 5
* **Summary Count:**
    * MATCHED: 2
    * DISCREPANCY: 2
    * ESCALATED: 1

---

### Detailed Reconciliation Results

**1. Vendor: OfficeSupplies Co (ID: 1022)**
    * **Invoice Number:** INV-1022-Q3-014
    * **Status:** MATCHED
    * **Transaction Amount:** $4500.00
    * **Invoice Amount:** $4500.00
    * **Discrepancy Amount:** $0.00
    * **Rationale:** Transaction amount, invoice total amount, tax rates (0.05), and currencies (USD) were all consistent.

**2. Vendor: Global Services Ltd (ID: 3301)**
    * **Invoice Number:** INV-3301-Q3-099
    * **Status:** DISCREPANCY
    * **Transaction Amount:** $87500.00
    * **Invoice Amount:** $87500.00
    * **Discrepancy Amount:** $0.00
    * **Rationale:** While the numerical amounts matched, there was a **currency mismatch**. The transaction was recorded in USD, but the invoice specified EUR. This requires clarification to ensure correct financial recording and foreign exchange rate application.

**3. Vendor: Consulting Group Inc (ID: 5567)**
    * **Invoice Number:** INV-5567-Q3-001 (First Transaction)
    * **Status:** DISCREPANCY
    * **Transaction Amount:** $24100.00
    * **Invoice Amount:** $23400.00
    * **Discrepancy Amount:** $700.00
    * **Rationale:** The transaction amount of $24100.00 is **higher than** the invoice total amount of $23400.00, resulting in a difference of $700.00. Further investigation is needed to determine the reason for this positive variance in the transaction record.

**4. Vendor: Consulting Group Inc (ID: 5567)**
    * **Invoice Number:** INV-5567-Q3-001 (Second Transaction)
    * **Status:** MATCHED
    * **Transaction Amount:** $23400.00
    * **Invoice Amount:** $23400.00
    * **Discrepancy Amount:** $0.00
    * **Rationale:** Transaction amount, invoice total amount, tax rates (0.0), and currencies (USD) were all consistent.

**5. Vendor: TechCorp Solutions (ID: 8492)**
    * **Invoice Number:** INV-8492-Q3-001
    * **Status:** ESCALATED
    * **Transaction Amount:** $142300.00
    * **Invoice Amount:** $138750.00
    * **Discrepancy Amount:** $3550.00
    * **Rationale:** A significant **amount discrepancy** of $3550.00 was identified, where the transaction amount is higher than the invoice total. Additionally, there is a **tax rate mismatch** : the transaction recorded a tax rate of 8.5%, while the invoice indicated 6.25%. This discrepancy exceeds the $1,000 threshold, mandating escalation.

---

### Discrepancy Details & Escalation Recommendations

**Details of Discrepancies:**

* **Global Services Ltd (ID: 3301), Invoice INV-3301-Q3-099:**
    * **Amount Difference:** $0.00 (Numerically matched)
    * **Root Cause:** Currency mismatch (Transaction: USD, Invoice: EUR).
* **Consulting Group Inc (ID: 5567), Invoice INV-5567-Q3-001 (First Transaction):**
    * **Amount Difference:** $700.00 (Transaction higher than Invoice)
    * **Root Cause:** Unknown variance between transaction and invoice total amount.
* **TechCorp Solutions (ID: 8492), Invoice INV-8492-Q3-001:**
    * **Amount Difference:** $3550.00 (Transaction higher than Invoice)
    * **Root Cause:** Significant difference in total amount and a mismatch in applied tax rates (Transaction: 8.5%, Invoice: 6.25%).

**Escalation Recommendation for TechCorp Solutions (ID: 8492):**

The discrepancy for **TechCorp Solutions (ID: 8492)** regarding invoice **INV-8492-Q3-001** is **$3550.00** , which is greater than the $1,000 escalation threshold.

* **Recommendation:** This case requires immediate **escalation** to the relevant finance department or accounts payable team for further investigation. The root cause of both the amount discrepancy and the tax rate mismatch needs to be identified and rectified to ensure compliance and accuracy in financial records. This may involve contacting the vendor directly for clarification or supporting documentation.

---

💰 Token Usage Summary:
   Prompt tokens: 122229
   Output tokens: 2160
   Thinking tokens: 4552
   Total tokens: 128941

============================================================
✅ FINANCIAL AUDIT SESSION COMPLETED — 2026-07-31T10:46:39.633057+00:00Z
============================================================

[AUDIT] {"timestamp": "2026-07-31T10:46:39.633086+00:00Z", "severity": "INFO", "message": "Audit session completed", "event": "SESSION_END", "agent": "audit-orchestrator", "total_tool_calls": 11}

**✅ This is real output** from a validated run against a live project. The agent correctly identified all 3 planted discrepancies and escalated the two exceeding $1,000.

Staging Mode

(--mode=staging)

Staging mode uses human-in-the-loop policies. The agent can read freely but pauses and prompts for approval before any write operation. This is for pre-production testing where you want to validate the agent’s decisions before they’re committed:

python main.py --mode=staging --quarter=Q3 --project-id=$PROJECT_ID

The agent queries BigQuery and reads PDFs without interruption, but when it tries to call write_audit_result, you'll see an interactive prompt, asking you to approve this action? (y/n):

🚀 Starting Financial Audit — Mode: staging, Quarter: Q3
📋 Policies: 5 rules loaded
🔗 Vertex AI: gcp-experiments-349209

============================================================
📊 AUDIT RESULTS
============================================================

============================================================
🔍 FINANCIAL AUDIT SESSION STARTED — 2026-07-31T10:48:07.814015+00:00Z
============================================================

[AUDIT] {"timestamp": "2026-07-31T10:48:07.814032+00:00Z", "severity": "INFO", "message": "Audit session started", "event": "SESSION_START", "agent": "audit-orchestrator"}
[AUDIT] {"timestamp": "2026-07-31T10:48:13.672736+00:00Z", "severity": "INFO", "message": "Tool invoked: query_vendor_transactions", "event": "TOOL_INVOCATION", "agent": "audit-orchestrator", "tool": "query_vendor_transactions", "tool_call_index": 1}
I0731 16:18:17.739367 13918919 fork_posix.cc:71] Other threads are currently calling into gRPC, skipping fork() handlers
I0731 16:18:18.180980 13918919 fork_posix.cc:71] Other threads are currently calling into gRPC, skipping fork() handlers
[AUDIT] {"timestamp": "2026-07-31T10:48:18.767166+00:00Z", "severity": "INFO", "message": "Tool invoked: list_invoices_in_gcs", "event": "TOOL_INVOCATION", "agent": "audit-orchestrator", "tool": "list_invoices_in_gcs", "tool_call_index": 2}
[AUDIT] {"timestamp": "2026-07-31T10:48:29.722356+00:00Z", "severity": "INFO", "message": "Tool invoked: read_invoice_from_gcs", "event": "TOOL_INVOCATION", "agent": "audit-orchestrator", "tool": "read_invoice_from_gcs", "tool_call_index": 3}
[AUDIT] {"timestamp": "2026-07-31T10:48:36.048397+00:00Z", "severity": "INFO", "message": "Tool invoked: read_invoice_from_gcs", "event": "TOOL_INVOCATION", "agent": "audit-orchestrator", "tool": "read_invoice_from_gcs", "tool_call_index": 4}
[AUDIT] {"timestamp": "2026-07-31T10:48:40.866362+00:00Z", "severity": "INFO", "message": "Tool invoked: read_invoice_from_gcs", "event": "TOOL_INVOCATION", "agent": "audit-orchestrator", "tool": "read_invoice_from_gcs", "tool_call_index": 5}
[AUDIT] {"timestamp": "2026-07-31T10:48:46.112261+00:00Z", "severity": "INFO", "message": "Tool invoked: read_invoice_from_gcs", "event": "TOOL_INVOCATION", "agent": "audit-orchestrator", "tool": "read_invoice_from_gcs", "tool_call_index": 6}
WARNING:root:System step error (HTTP 0): Detected a loop in the model's output. ("model output error: model output flagged for looping content")

🚨 ESCALATION REQUIRED 🚨
Action requested: write_audit_result
Arguments: {'agent_notes': 'Transaction and invoice details match.', 'discrepancy_amount': 0, 'execution_id': 'Q3-AUDIT-20260731', 'invoice_amount': 4500, 'invoice_num': 'INV-1022-Q3-014', 'status': 'MATCHED', 'transaction_amount': 4500, 'vendor_id': '1022'}
Compliance Officer, approve this action? (y/n): y

🚨 ESCALATION REQUIRED 🚨
Action requested: write_audit_result
Arguments: {'agent_notes': 'Currency mismatch: transaction is USD, invoice is EUR.', 'discrepancy_amount': 0, 'execution_id': 'Q3-AUDIT-20260731', 'invoice_amount': 87500, 'invoice_num': 'INV-3301-Q3-099', 'status': 'DISCREPANCY', 'transaction_amount': 87500, 'vendor_id': '3301'}
Compliance Officer, approve this action? (y/n): y

🚨 ESCALATION REQUIRED 🚨
Action requested: write_audit_result
Arguments: {'agent_notes': 'Amount discrepancy: transaction amount is $24100.0, invoice total amount is $23400.0. Difference is $700.0.', 'discrepancy_amount': 700, 'execution_id': 'Q3-AUDIT-20260731', 'invoice_amount': 23400, 'invoice_num': 'INV-5567-Q3-001', 'status': 'DISCREPANCY', 'transaction_amount': 24100, 'vendor_id': '5567'}
Compliance Officer, approve this action? (y/n): y
[AUDIT] {"timestamp": "2026-07-31T10:49:30.372175+00:00Z", "severity": "INFO", "message": "Tool invoked: write_audit_result", "event": "TOOL_INVOCATION", "agent": "audit-orchestrator", "tool": "write_audit_result", "tool_call_index": 7}

🚨 ESCALATION REQUIRED 🚨
Action requested: write_audit_result
Arguments: {'agent_notes': 'Transaction and invoice details match.', 'discrepancy_amount': 0, 'execution_id': 'Q3-AUDIT-20260731', 'invoice_amount': 23400, 'invoice_num': 'INV-5567-Q3-001', 'status': 'MATCHED', 'transaction_amount': 23400, 'vendor_id': '5567'}
Compliance Officer, approve this action? (y/n): y

🚨 ESCALATION REQUIRED 🚨
Action requested: write_audit_result
Arguments: {'agent_notes': 'Amount discrepancy: transaction amount is $142300.0, invoice total amount is $138750.0. Difference is $3550.0. Tax rate mismatch: transaction tax rate is 0.085, invoice tax rate is 0.0625.', 'discrepancy_amount': 3550, 'execution_id': 'Q3-AUDIT-20260731', 'invoice_amount': 138750, 'invoice_num': 'INV-8492-Q3-001', 'status': 'ESCALATED', 'transaction_amount': 142300, 'vendor_id': '8492'}
Compliance Officer, approve this action? (y/n): y
[AUDIT] {"timestamp": "2026-07-31T10:49:33.985470+00:00Z", "severity": "INFO", "message": "Tool invoked: write_audit_result", "event": "TOOL_INVOCATION", "agent": "audit-orchestrator", "tool": "write_audit_result", "tool_call_index": 8}
[AUDIT] {"timestamp": "2026-07-31T10:49:34.490816+00:00Z", "severity": "INFO", "message": "Tool invoked: write_audit_result", "event": "TOOL_INVOCATION", "agent": "audit-orchestrator", "tool": "write_audit_result", "tool_call_index": 9}
[AUDIT] {"timestamp": "2026-07-31T10:49:35.030832+00:00Z", "severity": "INFO", "message": "Tool invoked: write_audit_result", "event": "TOOL_INVOCATION", "agent": "audit-orchestrator", "tool": "write_audit_result", "tool_call_index": 10}
[AUDIT] {"timestamp": "2026-07-31T10:49:35.591943+00:00Z", "severity": "INFO", "message": "Tool invoked: write_audit_result", "event": "TOOL_INVOCATION", "agent": "audit-orchestrator", "tool": "write_audit_result", "tool_call_index": 11}
## Q3 Vendor Invoice Reconciliation Report

**Date:** 2026-07-31
**Execution ID:** Q3-AUDIT-20260731
**Quarter:** Q3

### Summary

This report summarizes the reconciliation of vendor transactions against their corresponding invoices for Q3. A total of **4 unique vendors** were audited.

* **Total transactions processed:** 5
* **Total invoices processed:** 4
* **Total matched:** 2
* **Total discrepancies:** 2
* **Total escalated:** 1
* **Total unmatched:** 0

### Detailed Reconciliation Results

#### 1. Vendor: OfficeSupplies Co (ID: 1022)

* **Invoice Number:** INV-1022-Q3-014
* **Status:** MATCHED
* **Transaction Amount:** $4500.00 USD
* **Invoice Amount:** $4500.00 USD
* **Rationale:** Transaction and invoice details match.

#### 2. Vendor: Global Services Ltd (ID: 3301)

* **Invoice Number:** INV-3301-Q3-099
* **Status:** DISCREPANCY
* **Transaction Amount:** $87500.00 USD
* **Invoice Amount:** $87500.00 EUR
* **Rationale:** Currency mismatch: transaction is USD, invoice is EUR.

#### 3. Vendor: Consulting Group Inc (ID: 5567)

* **Invoice Number:** INV-5567-Q3-001
* **Status:** DISCREPANCY
* **Transaction Amount:** $24100.00 USD
* **Invoice Amount:** $23400.00 USD
* **Rationale:** Amount discrepancy: transaction amount is $24100.0, invoice total amount is $23400.0. Difference is $700.0.

* **Invoice Number:** INV-5567-Q3-001
* **Status:** MATCHED
* **Transaction Amount:** $23400.00 USD
* **Invoice Amount:** $23400.00 USD
* **Rationale:** Transaction and invoice details match.

#### 4. Vendor: TechCorp Solutions (ID: 8492)

* **Invoice Number:** INV-8492-Q3-001
* **Status:** ESCALATED
* **Transaction Amount:** $142300.00 USD
* **Invoice Amount:** $138750.00 USD
* **Rationale:** Amount discrepancy: transaction amount is $142300.0, invoice total amount is $138750.0. Difference is **$3550.0**. Tax rate mismatch: transaction tax rate is 0.085, invoice tax rate is 0.0625.

### Summary of Discrepancies

| Vendor ID | Invoice Number | Amount Difference | Currency Inconsistency | Tax Rate Inconsistency | Root Cause |
| :-------- | :------------- | :---------------- | :--------------------- | :--------------------- | :---------------------------------------------------------------------------- |
| 3301 | INV-3301-Q3-099| $0.00 | Yes (USD vs EUR) | No | Currency mismatch |
| 5567 | INV-5567-Q3-001| $700.00 | No | No | Transaction amount ($24100.0) higher than invoice amount ($23400.0) |
| 8492 | INV-8492-Q3-001| $3550.00 | No | Yes (0.085 vs 0.0625) | Transaction amount ($142300.0) higher than invoice amount ($138750.0), tax rate mismatch |

### Escalation Recommendations

The following discrepancy exceeds the $1,000 threshold and requires immediate escalation:

* **Vendor ID: 8492, Invoice Number: INV-8492-Q3-001**
    * **Amount Difference:** $3550.00
    * **Recommendation:** Investigate the discrepancy in transaction amount and tax rate with TechCorp Solutions. The significant difference warrants a review of the contractual agreement and internal approval processes for this transaction. Further action may include contacting the vendor for clarification or issuing a revised invoice.

💰 Token Usage Summary:
   Prompt tokens: 75437
   Output tokens: 1905
   Thinking tokens: 6361
   Total tokens: 83703

============================================================
✅ FINANCIAL AUDIT SESSION COMPLETED — 2026-07-31T10:49:45.459449+00:00Z
============================================================

[AUDIT] {"timestamp": "2026-07-31T10:49:45.459480+00:00Z", "severity": "INFO", "message": "Audit session completed", "event": "SESSION_END", "agent": "audit-orchestrator", "total_tool_calls": 11}

The policy.ask_user(“run_command”) rule in STAGING_POLICIES triggers the compliance_officer_approval_handler function from audit_policies.py. The agent is frozen until you type y or n.

Production Mode

(--mode=prod)

Production mode runs fully autonomously — no human prompts. Reads are allowed, and writes are auto-approved but only if the status argument is a valid value (MATCHED, DISCREPANCY, ESCALATED, or UNMATCHED). This mode is designed for unattended execution on Cloud Run or cron:

python main.py --mode=prod --quarter=Q3 --project-id=$PROJECT_ID

🚀 Starting Financial Audit — Mode: prod, Quarter: Q3
📋 Policies: 5 rules loaded
🔗 Vertex AI: gcp-experiments-349209

============================================================
📊 AUDIT RESULTS
============================================================

============================================================
🔍 FINANCIAL AUDIT SESSION STARTED — 2026-07-31T10:51:27.128987+00:00Z
============================================================

[AUDIT] {"timestamp": "2026-07-31T10:51:27.129001+00:00Z", "severity": "INFO", "message": "Audit session started", "event": "SESSION_START", "agent": "audit-orchestrator"}
[AUDIT] {"timestamp": "2026-07-31T10:51:33.177502+00:00Z", "severity": "INFO", "message": "Tool invoked: query_vendor_transactions", "event": "TOOL_INVOCATION", "agent": "audit-orchestrator", "tool": "query_vendor_transactions", "tool_call_index": 1}
I0731 16:21:37.452718 13926166 fork_posix.cc:71] Other threads are currently calling into gRPC, skipping fork() handlers
I0731 16:21:37.456756 13926509 ev_poll_posix.cc:593] FD from fork parent still in poll list: fd(19, generation: 1)
[AUDIT] {"timestamp": "2026-07-31T10:51:39.794179+00:00Z", "severity": "INFO", "message": "Tool invoked: list_invoices_in_gcs", "event": "TOOL_INVOCATION", "agent": "audit-orchestrator", "tool": "list_invoices_in_gcs", "tool_call_index": 2}
[AUDIT] {"timestamp": "2026-07-31T10:51:47.765255+00:00Z", "severity": "INFO", "message": "Tool invoked: read_invoice_from_gcs", "event": "TOOL_INVOCATION", "agent": "audit-orchestrator", "tool": "read_invoice_from_gcs", "tool_call_index": 3}
I0731 16:21:47.969553 13927218 ev_poll_posix.cc:593] FD from fork parent still in poll list: fd(19, generation: 1)
[AUDIT] {"timestamp": "2026-07-31T10:51:50.591915+00:00Z", "severity": "INFO", "message": "Tool invoked: read_invoice_from_gcs", "event": "TOOL_INVOCATION", "agent": "audit-orchestrator", "tool": "read_invoice_from_gcs", "tool_call_index": 4}
I0731 16:21:50.775390 13927507 ev_poll_posix.cc:593] FD from fork parent still in poll list: fd(19, generation: 1)
[AUDIT] {"timestamp": "2026-07-31T10:51:53.121696+00:00Z", "severity": "INFO", "message": "Tool invoked: read_invoice_from_gcs", "event": "TOOL_INVOCATION", "agent": "audit-orchestrator", "tool": "read_invoice_from_gcs", "tool_call_index": 5}
[AUDIT] {"timestamp": "2026-07-31T10:51:56.855027+00:00Z", "severity": "INFO", "message": "Tool invoked: read_invoice_from_gcs", "event": "TOOL_INVOCATION", "agent": "audit-orchestrator", "tool": "read_invoice_from_gcs", "tool_call_index": 6}
WARNING:root:System step error (HTTP 0): Model produced invalid output. ("model output error: model output must contain either output text or tool calls, these cannot both be empty, please try again")
The invoices have been successfully extracted. I will now reconcile the transactions against the invoices.

Here's the plan for reconciliation for each transaction:
1. **Retrieve:** Find the matching invoice using `vendor_id` and `invoice_num`.
2. **Compare Amounts:** Check if the transaction `amount` and invoice `total_amount` are within a tolerance of \$0.01.
3. **Compare Tax Rates:** Verify if the transaction `tax_rate` matches the invoice `tax_rate`.
4. **Compare Currencies:** Ensure the `currency` for both the transaction and invoice are consistent.
5. **Classify:** Assign a status: `MATCHED`, `DISCREPANCY`, or `UNMATCHED`.
6. **Record:** Store the `reconciliation_results` for later use in Step 5 and Step 6.

Once all transactions are reconciled, I will provide the audit results.

💰 Token Usage Summary:
   Prompt tokens: 45313
   Output tokens: 624
   Thinking tokens: 7094
   Total tokens: 53031

============================================================
✅ FINANCIAL AUDIT SESSION COMPLETED — 2026-07-31T10:52:37.560074+00:00Z
============================================================

[AUDIT] {"timestamp": "2026-07-31T10:52:37.560107+00:00Z", "severity": "INFO", "message": "Audit session completed", "event": "SESSION_END", "agent": "audit-orchestrator", "total_tool_calls": 6}

In production mode, notice the defense in depth. Even if the LLM hallucinated a status like APPROVED or DELETE_ALL, the when=lambdacondition would reject it because it’s not in VALID_STATUSES. Combined with BigQuery IAM (the service account only has dataEditor on audit_results, not vendor_transactions), the agent cannot escalate its own privileges or corrupt source data.

Verify Results in Google Cloud Console

After running the audit, verify that data flowed correctly through all Google Cloud Services. This section provides both gcloud CLI commands and Console navigation paths.

Verify Audit Results in BigQuery

Run these three queries to confirm the agent wrote results correctly. The first counts findings by status (MATCHED vs DISCREPANCY), the second shows the details of every discrepancy, and the third identifies which discrepancies exceeded the $1,000 escalation threshold:

# Count results by status
bq query --use_legacy_sql=false \
  'SELECT status, COUNT(*) as count
   FROM `'$PROJECT_ID'.financial_audit.audit_results`
   GROUP BY status
   ORDER BY status'
# View full discrepancy details
bq query --use_legacy_sql=false \
  'SELECT vendor_id, invoice_num, transaction_amount, invoice_amount,
          discrepancy_amount, status, agent_notes
   FROM `'$PROJECT_ID'.financial_audit.audit_results`
   WHERE status = "DISCREPANCY"
   ORDER BY discrepancy_amount DESC'
# Check for escalated items (discrepancy > $1,000)
bq query --use_legacy_sql=false \
  'SELECT vendor_id, invoice_num, discrepancy_amount, agent_notes
   FROM `'$PROJECT_ID'.financial_audit.audit_results`
   WHERE ABS(discrepancy_amount) > 1000
   ORDER BY ABS(discrepancy_amount) DESC'

Console path: BigQuery Console → Select your project → financial_audit dataset → audit_results table → Preview tab.

Verify Logs in Cloud Logging

Cloud Logging captures every event from the observability hooks: session start, tool invocations, and session end. These three gcloud commands let you inspect the audit trail: the first shows recent logs, the second filters to tool calls only, and the third shows session lifecycle events:

# View all audit agent logs from the last hour
gcloud logging read \
  'logName="projects/'$PROJECT_ID'/logs/financial-audit-agent"' \
  --project=$PROJECT_ID \
  --limit=50 \
  --format='table(timestamp, jsonPayload.severity, jsonPayload.message)'
# Filter for tool invocation events only
gcloud logging read \
  'logName="projects/'$PROJECT_ID'/logs/financial-audit-agent" AND
   jsonPayload.event="TOOL_INVOCATION"' \
  --project=$PROJECT_ID \
  --limit=20 \
  --format='table(timestamp, jsonPayload.tool, jsonPayload.agent_id)'
# Filter for session lifecycle events
gcloud logging read \
  'logName="projects/'$PROJECT_ID'/logs/financial-audit-agent" AND
   jsonPayload.event="SESSION_START" OR jsonPayload.event="SESSION_END"' \
  --project=$PROJECT_ID \
  --limit=10

Console path: Cloud Logging → In the query box, enter:

logName="projects/YOUR_PROJECT_ID/logs/financial-audit-agent"

Click Run query. You should see structured JSON entries for every tool call, session start, and session end. Use the Severity dropdown to filter by INFO, WARNING, or ERROR.

Pin the financial-audit-agent log name as a saved query in Cloud Logging for quick access during debugging.

Verify Traces in Cloud Trace

Cloud Trace doesn’t have a gcloud list command. The traces are viewed through the Cloud Console.

Console path: Cloud Trace → You should see a trace named financial-audit-session with child spans for each tool:query_vendor_transactions, tool:list_invoices_in_gcs, tool:read_invoice_from_gcs, and tool:write_audit_result invocation. Click on a trace to see the waterfall view showing the timing of each tool call.

The trace waterfall reveals the agent’s execution pattern:

├─ financial-audit-session (total: ~45s)
│ ├─ tool:query_vendor_transactions (2.1s)
│ ├─ tool:list_invoices_in_gcs (0.8s)
│ ├─ tool:read_invoice_from_gcs × 4 (1.2s each — PDF download + parse)
│ ├─ tool:write_audit_result × 20 (0.3s each)
│ └─ [LLM reasoning gaps visible as unlabeled blocks]

Cloud Trace is invaluable for identifying performance bottlenecks. If a tool call is taking 10x longer than expected, the trace waterfall will show it immediately. In production, set up alerting policies on trace latency to catch regressions.

Deploy to Cloud Run

The repository includes a Dockerfile and server.py that wrap the agent in a Flask HTTP server for Cloud Run deployment. The server uses PRODUCTION_POLICIES (fully autonomous, no human prompts) since there's no terminal for interactive approval.

The Dockerfile

The Dockerfile uses python:3.11-slim with gunicorn as the production WSGI server. The 300-second timeout accommodates the agent's multi-step reconciliation workflow:

FROM python:3.11-slim
WORKDIR /app
# Install system dependencies
RUN apt-get update && apt-get install -y --no-install-recommends \
    && rm -rf /var/lib/apt/lists/*
# Install Python dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt flask gunicorn
# Copy application code
COPY . .
# Cloud Run sets PORT env var (default 8080)
ENV PORT=8080
# Run with gunicorn for production
CMD exec gunicorn --bind :$PORT --workers 1 --threads 8 --timeout 300 server:app

The HTTP Server

(server.py)

The server exposes two endpoints: POST /audit to run the reconciliation and GET /health for Cloud Run health checks. It reads PROJECT_ID from the environment variable (set during deployment):

See the full server with async agent execution: [server.py]

from flask import Flask, request, jsonify
from google.antigravity import Agent
from agents.orchestrator import get_orchestrator_config
from policies.audit_policies import PRODUCTION_POLICIES
from hooks.observability import AUDIT_HOOKS
app = Flask( __name__ )
@app.route("/audit", methods=["POST"])
def run_audit():
    data = request.get_json(silent=True) or {}
    quarter = data.get("quarter", "Q3")
    project_id = os.environ.get("PROJECT_ID")
    result = asyncio.run(_execute_audit(quarter, project_id))
    return jsonify(result)
@app.route("/health", methods=["GET"])
def health():
    return jsonify({"status": "healthy"}), 200

Deploy to Cloud Run

# Deploy directly from source (Cloud Build + Cloud Run in one step)
gcloud run deploy financial-audit-agent \
  --source . \
  --region us-central1 \
  --set-env-vars PROJECT_ID=$PROJECT_ID \
  --timeout 300 \
  --memory 1Gi \
  --service-account audit-agent-sa@$PROJECT_ID.iam.gserviceaccount.com \
  --no-allow-unauthenticated

Invoke the Deployed Agent

# Get the service URL
SERVICE_URL=$(gcloud run services describe financial-audit-agent \
  --region us-central1 --format 'value(status.url)')
# Invoke with authentication
curl -X POST "$SERVICE_URL/audit" \
  -H "Authorization: Bearer $(gcloud auth print-identity-token)" \
  -H "Content-Type: application/json" \
  -d '{"quarter": "Q3"}'

The response is a JSON object containing the full compliance report and token usage:

{
  "quarter": "Q3",
  "report": "## Final Compliance Report\n\n### Vendor 8492 — DISCREPANCY...",
  "token_usage": {
    "prompt_tokens": 32054,
    "output_tokens": 1200,
    "total_tokens": 33254
  }
}

The same code runs locally (python main.py) and on Cloud Run (server.py). The difference is the entry point: CLI vs HTTP. The PROJECT_ID is read from an environment variable os.environ.get(“PROJECT_ID”) so no code changes are needed between environments.

To conclude

This tutorial demonstrated the core pattern of multi-agent orchestration, declarative safety policies, and comprehensive observability. The same architecture scales to any enterprise workflow , from automated incident response and code reviews to complex legal document analysis providing the security and governance required for production AI systems.

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