Agentic Analytics on BigQuery: Turning SQL Warehouses into AI Analysts

Agentic analytics transforms BigQuery from a passive query engine into an autonomous analyst that interprets natural language, generates accurate SQL, and produces insights without manual intervention. Current approaches fail because AI models hallucinate table names, write incorrect joins, and produce inconsistent metric definitions. The path forward combines semantic metadata, structured tool calling, and progressive validation patterns to create reliable agents. BigQuery's native features—metadata APIs, JSON outputs, and partition intelligence—provide the infrastructure needed for production agentic analytics.

What Agentic Analytics Means for Data Warehouses

Agentic analytics refers to AI systems that autonomously query and analyze data warehouses based on natural language requests. Rather than analysts writing SQL manually, an AI agent interprets questions, determines appropriate data sources, constructs queries, executes them, and presents results with context.

On BigQuery, this means an agent receives "What caused the revenue decline last quarter?" and executes the necessary sequence of queries to answer—joining transaction tables, comparing time periods, identifying segments with largest drops, and formatting findings without human SQL authorship.

The agent operates as an autonomous analyst:

Interprets ambiguous questions into specific analytical tasks
Selects appropriate tables from hundreds or thousands of options
Constructs correct joins across fact and dimension tables
Applies business logic like excluding test data or handling refunds
Formats results with context and follow-up suggestions

This shifts data analysis from "write SQL to answer question" to "ask question, get answer."

How Teams Run Analytics on BigQuery Today

Manual SQL Authorship

Analysts write SQL queries directly in BigQuery Console, notebooks, or local editors.

A typical workflow:

sql
SELECT
  c.region,
  SUM(o.amount) as total_revenue
FROM `project.dataset.orders` o
JOIN `project.dataset.customers` c
  ON o.customer_id = c.customer_id
WHERE o.order_date >= '2025-07-01'
  AND o.order_date < '2025-10-01'
GROUP BY c.region
ORDER BY total_revenue DESC;

This requires analysts to know:

Exact table paths in project hierarchies
Column names and types across tables
Join keys and relationship cardinality
Business rules for date ranges and filters
Partition columns for cost optimization

Analysts spend 60-70% of time writing query variations and debugging join logic rather than interpreting results or making recommendations.

BI Tool Visual Builders

Platforms provide drag-and-drop interfaces that generate SQL behind the scenes. Users select dimensions and measures from menus, apply filters through dropdowns, and receive visualizations without SQL knowledge.

The tool maintains a semantic model mapping user-friendly labels to underlying database structures. When a user selects "Revenue by Region," the tool generates appropriate SQL.

Constraints:

Limited to pre-modeled dimensions and measures
Custom calculations require learning tool-specific languages
Each tool maintains separate metric definitions
No conversational interface—users must know available fields
Complex analysis requires falling back to manual SQL

LLM SQL Generation

The first wave of AI analytics: feed natural language questions to language models, receive SQL, execute queries.

Basic pattern:

python
prompt = f"""
BigQuery tables:
- orders (order_id, customer_id, amount, order_date)
- customers (customer_id, name, region)

Write SQL for: {user_question}
"""

sql = llm_generate(prompt)
results = bigquery_client.query(sql).result()

This approach fails in production because models lack schema knowledge and invent non-existent tables, write incorrect join conditions, and produce different calculations for identical questions.

Accuracy rates for queries requiring multiple joins sit around 35-45% without schema constraints.

Database Assistant Features

Some platforms offer built-in AI assistants that suggest queries based on natural language input. These tools access schema metadata and recognize common query patterns.

Improvements over raw LLM generation:

Schema awareness prevents some hallucination
Pattern recognition suggests optimized structures
Integration with platform-specific features

Remaining issues:

Prone to errors on schemas with hundreds of tables
Limited to suggestion mode, not full execution
No retention of business logic or metric standards
Variable quality across similar questions

Why Current Approaches Fail

Hallucinated Identifiers

Language models guess table and column names based on training patterns, not actual schema. When asked about unfamiliar structures, they assume common naming conventions.

Example failure:

Question: "Show top customers by lifetime value"

Model assumes:

customers table
orders table
Pre-computed customer_lifetime_value column

Actual schema:

prod_data.dim_customer
prod_data.fact_transactions
Lifetime value requires calculation across multiple tables

The query fails with "Table not found" or returns incorrect results if similar names exist elsewhere.

In enterprise BigQuery environments with 500+ tables, models hallucinate table references over 60% of the time and column names over 40% when querying unfamiliar datasets without schema constraints.

Inconsistent Metric Definitions

Different analysts calculate identical metrics differently. AI amplifies this by generating new variations each request.

A real example from a mid-stage company:

"Revenue" appears in five forms:

SUM(amount) including refunds
SUM(amount - refund_amount) correct definition
SUM(CASE WHEN status = 'completed' THEN amount ELSE 0 END) missing partial completions
SUM(amount) * 0.95 arbitrary discount assumption
SUM(amount) WHERE order_type != 'internal' partial exclusion

An AI agent without grounding produces variation six: SUM(total_price) using pre-tax amounts.

Result: Executive dashboard shows revenue 12% different from finance report. Data teams spend weeks reconciling only to find definitional inconsistency caused the gap.

Incorrect Join Logic

Proper joins require understanding relationship types, avoiding duplicate rows, and choosing efficient join order.

Where models fail:

Fan-out creation: Agent joins orders to products to categories. Products belong to multiple categories, creating duplicate order rows. Revenue counts multiple times.

Missing intermediate tables: To get customer region from orders requires joining through customers table. Agent attempts direct join, fails to find matching keys.

Wrong join path selection: Multiple valid paths exist (orders to customers via customer_id vs billing_customer_id). Agent picks incorrectly, returns plausible but wrong results.

Testing 100 queries requiring 2+ joins on realistic schemas showed 55-60% incorrect results from language models using basic prompting.

Performance Disasters

Models optimize for query correctness, not efficiency. Generated SQL frequently scans entire tables unnecessarily.

Common anti-patterns:

Skipped partition filters: Tables partitioned by date, but WHERE clause doesn't reference partition key. Query scans entire history instead of relevant range.

Unfiltered join inputs: Joins large tables before filtering, when filtering first reduces join inputs by 90%+.

Unnecessary aggregation layers: Computes intermediate aggregations that could be eliminated through query rewriting.

Cost impact example:

Analyst-written SQL: 2.5 GB scanned, $0.0125 cost, 1.2 second execution

Model-generated SQL for identical question: 180 GB scanned, $0.90 cost, 23 second execution

At 1000 queries daily, this means $270,000 additional annual BigQuery costs plus user frustration from slow responses.

Missing Governance Controls

Production analytics requires access control, audit logging, and validation. Direct model-to-SQL execution bypasses these safeguards.

Security gaps:

Unrestricted table access: Agent queries any table service account can reach, potentially exposing sensitive data to unauthorized users.

No query validation: Malicious or careless questions trigger expensive queries, data modification statements (if permissions allow), or inappropriate table combinations.

Absent audit trails: No record linking questions to queries, making debugging and security investigations impossible.

Compliance violations: Regulations require tracking data access. Natural language interfaces obscure this without explicit instrumentation.

Building Production-Grade Agentic Analytics

Semantic Metadata as Agent Foundation

Reliable agentic analytics requires giving agents structured metadata about available metrics, dimensions, and relationships rather than free-form SQL generation.

Architecture:

User Question → AI Agent → Semantic Metadata → SQL Generation → BigQuery → Results

Instead of guessing table names, agents query metadata catalogs defining available metrics with explicit business logic.

Without metadata:

Agent prompt: "Write SQL to calculate revenue"
Agent output: Invents table names and calculation logic

With metadata:

Agent prompt: "Available metrics: revenue, orders, customers. Revenue = SUM(orders.amount - orders.refund_amount) WHERE status='completed'"
Agent output: Requests defined metric through API

Semantic metadata translates metric requests into correct SQL with proper tables, joins, and business rules.

BigQuery implementation approaches:

Annotated views: Create views exposing metrics, use DESCRIPTION and LABELS to document business logic for agent consumption.

Information schema queries: Agents query INFORMATION_SCHEMA.TABLES, COLUMNS, and TABLE_OPTIONS to learn schema before SQL generation.

External metadata systems: APIs provide metric definitions, agents query these before constructing BigQuery SQL.

Schema Documentation Practices

Comprehensive documentation reduces hallucination by giving agents accurate table and column information.

BigQuery native capabilities:

Table descriptions:

sql
ALTER TABLE `project.dataset.orders`
SET OPTIONS (
  description="Customer orders fact table. One row per order. Join to customers via customer_id.",
  labels=[("domain", "sales"), ("sensitivity", "internal")]
);

Column descriptions:

sql
ALTER TABLE `project.dataset.orders`
ALTER COLUMN amount SET OPTIONS (
  description="Order amount USD pre-tax. Null for cancelled orders."
);

Agent prompt construction:

python
# Retrieve schema with metadata
schema_query = """
SELECT
  table_name,
  column_name,
  data_type,
  description
FROM `project.dataset.INFORMATION_SCHEMA.COLUMN_FIELD_PATHS`
WHERE table_schema = 'dataset'
"""

schema_info = bigquery_client.query(schema_query).to_dataframe()

# Include in agent context
prompt = f"""
Schema documentation:
{schema_info.to_markdown()}

User question: {user_question}
Use exact table and column names from schema above.
"""

Documentation standards:

Explicit join documentation: "To get customer region from orders: JOIN orders.customer_id = customers.customer_id, then reference customers.region"

Business rule clarification: "Revenue excludes status='cancelled' and status='test'. Filter order_date >= '2023-01-01' to avoid legacy data quality issues."

Partition information: "Table partitioned by order_date. Include order_date filter to prevent full scan."

Query examples: "Active users pattern: COUNT(DISTINCT user_id) WHERE last_activity_date >= CURRENT_DATE() - 30"

Structured Tool Calling

Instead of free-form SQL generation, provide agents with structured tools enforcing correct patterns.

Tool-based implementation:

python
# Define constrained operations
tools = [
    {
        "name": "query_metric",
        "description": "Query predefined business metric",
        "parameters": {
            "metric_name": {"type": "string", "enum": ["revenue", "orders", "customers"]},
            "dimensions": {"type": "array", "items": {"type": "string"}},
            "filters": {"type": "object"},
            "date_range": {"type": "object"}
        }
    },
    {
        "name": "query_table",
        "description": "Query specific table for custom analysis",
        "parameters": {
            "table_name": {"type": "string", "enum": ["orders", "customers", "products"]},
            "columns": {"type": "array"},
            "filters": {"type": "object"}
        }
    }
]

# Agent uses structured calls
response = agent.execute(
    question="What was Q3 revenue by region?",
    tools=tools
)

# Agent returns structured call:
# query_metric(metric_name="revenue", dimensions=["region"], date_range={"start": "2025-07-01", "end": "2025-09-30"})

# System generates validated SQL
sql = build_sql_from_tool_call(response)

Benefits:

Type validation: Parameters checked before SQL generation, preventing invalid references.

Limited scope: Agent restricted to allowed tables and operations, preventing unauthorized access.

Audit capability: Tool calls logged with parameters, creating clear question-to-query trail.

Pre-execution validation: Each call validated against governance rules before execution.

Progressive Query Patterns

Break complex analyses into validated steps rather than single large queries.

Pattern: Clarification first

python
# Generate execution plan without immediate query
plan = agent.plan(question="Show top customers")

# Agent identifies missing context:
{
  "needs_clarification": true,
  "questions": [
    "Top by revenue, order count, or lifetime value?",
    "Time period: last 30 days, last year, or all time?"
  ]
}

# Collect clarifications, then execute with full context

Pattern: Multi-step with validation

python
# Step 1: Identify required tables
tables = agent.identify_tables(question)
# Returns: ["orders", "customers"]

# Step 2: Check access permissions
if not user_authorized(tables):
    return "Access denied to required tables"

# Step 3: Generate SQL
sql = agent.generate(question, allowed_tables=tables)

# Step 4: Validate query structure
if not validate_sql(sql):
    return "Generated query failed validation"

# Step 5: Execute
results = bigquery_client.query(sql)

Pattern: Explain before execution

python
# Generate execution explanation
explanation = agent.explain(sql)
# "This query: (1) Joins orders to customers on customer_id,
#  (2) Filters Q3 2025 orders, (3) Sums revenue by region,
#  (4) Scans approximately 50GB"

# Show to user with cost estimate
print(f"Execution plan: {explanation}")
print(f"Estimated cost: $0.25")

# Require confirmation for expensive queries
if user_confirms():
    execute()

BigQuery-Specific Optimizations

Leverage BigQuery features to improve agentic analytics reliability and performance.

JSON output for structured results:

sql
-- Return structured data for easier parsing
SELECT TO_JSON_STRING(STRUCT(
  region,
  revenue,
  RANK() OVER (ORDER BY revenue DESC) as rank
)) as result_json
FROM query_results

Agents parse JSON more reliably than CSV or tabular formats, reducing post-processing errors.

Cost estimation before execution:

python
# Dry run to estimate scan size
job_config = bigquery.QueryJobConfig(dry_run=True)
query_job = client.query(sql, job_config=job_config)

bytes_scanned = query_job.total_bytes_processed
estimated_cost = (bytes_scanned / 1e12) * 5  # $5 per TB

# Block expensive queries
if estimated_cost > threshold:
    return f"Query cost ${estimated_cost} exceeds limit. Refine question."

Parameterized query templates:

python
# Pre-built templates for common patterns
templates = {
    "revenue_by_dimension": """
        SELECT
          {dimension},
          SUM(amount - refund_amount) as revenue
        FROM `project.dataset.orders`
        WHERE order_date >= @start_date
          AND order_date <= @end_date
        GROUP BY {dimension}
    """
}

# Agent fills parameters instead of writing SQL from scratch
sql = templates["revenue_by_dimension"].format(dimension="region")
job_config = bigquery.QueryJobConfig(
    query_parameters=[
        bigquery.ScalarQueryParameter("start_date", "DATE", "2025-07-01"),
        bigquery.ScalarQueryParameter("end_date", "DATE", "2025-09-30"),
    ]
)

Query result caching:

python
# Check cache before executing
cache_key = hash_query_canonical_form(sql)
if cache_key in cache and not expired(cache_key):
    return cached_results

# Execute and cache if not found
results = bigquery_client.query(sql)
cache[cache_key] = results

Validation and Testing Framework

Production systems require systematic validation to catch errors before user impact.

Pre-execution checks:

python
def validate_query(sql):
    # Only allow SELECT statements
    if not sql.strip().upper().startswith('SELECT'):
        return False, "Only SELECT queries permitted"

    # Block data modification
    forbidden = ['DELETE', 'UPDATE', 'DROP', 'TRUNCATE', 'INSERT']
    if any(keyword in sql.upper() for keyword in forbidden):
        return False, "Data modification not allowed"

    # Require partition filters on large tables
    if 'fact_orders' in sql and 'order_date' not in sql:
        return False, "Must filter by order_date partition key"

    # Verify table access
    tables = extract_tables(sql)
    if not all(authorized(table) for table in tables):
        return False, f"Unauthorized table access: {tables}"

    # Check cost estimate
    cost = estimate_cost(sql)
    if cost > max_cost:
        return False, f"Cost ${cost} exceeds limit"

    return True, "Validation passed"

Post-execution validation:

python
# Alert on suspicious results
if results.total_rows == 0:
    log_warning("Empty result set", sql, question)

if results.total_rows > 1_000_000:
    log_warning("Unexpectedly large result", sql, question)

# Check null rates
null_rates = calculate_null_percentages(results)
if any(rate > 0.5 for rate in null_rates.values()):
    log_warning("High null percentage", sql, question)

Regression test suite:

python
# Maintain question-to-expected-SQL mappings
test_cases = [
    {
        "question": "Q3 revenue by region",
        "expected_tables": ["orders", "customers"],
        "expected_metrics": ["revenue"],
        "expected_dimensions": ["region"],
        "expected_filters": ["order_date >= '2025-07-01'"]
    }
]

# Run after agent changes
for test in test_cases:
    generated_sql = agent.generate(test["question"])
    assert all(table in generated_sql for table in test["expected_tables"])
    assert "SUM(" in generated_sql  # Aggregation present

Future Direction

Self-Improving Query Patterns

Next-generation agents will monitor performance and adapt strategies based on observed results.

Agents track query failures and unexpected outputs, adjusting prompts to avoid similar issues. After repeated "table not found" errors, agents learn to verify table existence through metadata queries before SQL generation.

Performance adaptation: Agents measure execution time and bytes scanned for different SQL approaches, then optimize future queries based on cost observations. After detecting that pre-filtering reduces scan volume 10x, agents apply this pattern systematically.

Autonomous error recovery: When queries fail, agents analyze error messages, hypothesize corrections (wrong table name, missing join key, incompatible types), and attempt fixes without user intervention.

Multi-Agent Workflows

Complex analysis tasks decompose into specialized agents collaborating.

Architecture:

Planner agent: Receives questions, breaks into sub-tasks, determines execution sequence.

Schema agent: Queries metadata systems, interprets table documentation, identifies relevant structures.

SQL agent: Writes optimized BigQuery SQL for specific analytical tasks.

Validator agent: Reviews generated SQL, checks constraints, estimates costs, approves execution.

Interpreter agent: Analyzes results, generates insights, handles follow-ups.

Example workflow:

Question: "Why did revenue drop in Q3?"

Planner: Decompose into (1) Q3 vs Q2 comparison, (2) Identify top negative contributors

Schema agent: Relevant tables = orders, products, regions

SQL agent: Generate comparative query with product and regional breakdown

Validator: Approve (cost $0.15, 8GB scan)

Execute

Interpreter: "Revenue dropped 8% ($2.3M to $2.1M). Main driver: Consumer Electronics
down 22% in Northeast. Correlates with competitor launch Aug 1."

Each agent specializes, improving accuracy through focused expertise while coordination ensures coherent analysis.

Conversational Analytics

Moving beyond isolated questions to stateful conversations with context retention.

Context awareness:

User: "Show Q3 revenue by region"
Agent: [Executes, displays results]

User: "Now year-over-year growth"
Agent: [Understands reference to previous metrics and dimensions, generates comparison]

User: "Focus on Northeast"
Agent: [Applies filter while maintaining query context]

User: "What products drove decline?"
Agent: [Adds product dimension to filtered query]

Agents maintain conversation state, interpreting pronouns and relative references without requiring full question restatement.

Proactive analysis:

After showing Q3 revenue decline, agents offer:

"Revenue dropped in Q3. Would you like me to:

Compare to Q2 and identify largest decreases
Show regional breakdown
Analyze by product category
Check if volume or price drove change"

Users select options, agents execute based on initial analysis rather than waiting for explicit requests.

Dashboard Integration

Agents augment existing dashboards with on-demand deep dives.

Agent-powered drill-down:

User viewing dashboard sees "Revenue: $2.1M (-8%)" with decline indicator. Clicks "Analyze" button.

Dashboard invokes agent with context: metric=revenue, period=Q3, issue=decline.

Agent analyzes: compares periods, breaks down segments, identifies outliers, returns formatted explanation displayed in dashboard.

Automated anomaly detection:

Dashboards query standard metrics but also invoke agents to scan for unusual patterns:

"Agent detected: Customer acquisition cost increased 35% in August while conversion rate remained flat. Ad spend grew without corresponding results. Investigate campaigns?"

User accepts, agent performs multi-step analysis across marketing, orders, and customer data, surfaces specific campaigns with poor ROI.

Predictive Query Optimization

Agents anticipate needs and pre-compute expensive queries.

Pattern recognition:

Agent observes user requesting "monthly revenue" at 9am each Monday. Pre-computes at 8am, caches results, returns instantly when requested.

Detects quarter-end pattern: historical data shows Q3 close triggers surge in "regional performance" queries. Proactively refreshes regional metrics.

Intelligent caching:

Instead of caching raw results, agents identify reusable intermediate computations:

Query A needs: orders_last_90_days Query B needs: orders_last_90_days + customer_join Query C needs: orders_last_90_days + product_join

Agent caches orders_last_90_days as materialized result. Queries B and C compute only their specific joins rather than re-scanning orders.

How Typedef Helps

Building agentic analytics requires infrastructure that grounds AI agents in accurate metadata and validated business logic. Typedef provides the semantic foundation that prevents hallucination and ensures agents generate correct SQL.

When your team needs to move beyond basic query generation to production-grade agentic analytics, Typedef helps establish the structured metadata layer that makes AI-powered analysis reliable at scale.