AI data agents can make it much easier for users to work with large datasets. A user can ask a question in natural language, and the agent can interpret the request, generate SQL, execute it against a data warehouse, and return an answer.
That convenience creates a new challenge for engineering teams.
A traditional application can often be evaluated using request latency, error rates, throughput, and infrastructure utilization. An AI data agent needs additional measurements because the quality of its behavior depends on both the model and the database operations it generates.
An agent might return the correct answer but scan too much data. It might generate an efficient query but take too long to reason through a complex request. It might also produce a fast response that is based on an incorrect interpretation of the user's question.
For production systems, query cost and latency should therefore be evaluated together with agent behavior and answer quality.
What Is an AI Data Agent?
An AI data agent combines a language model with tools that allow it to access and work with structured data.
A simplified architecture looks like this:
User Question
|
v
AI Data Agent
|
+---- Understand request
|
+---- Select data source
|
+---- Generate SQL
|
v
BigQuery
|
v
Query Results
|
v
Agent Interpretation
|
v
Natural Language Response
The important point is that the user's request can trigger multiple operations.
For example, a single question might cause the agent to inspect a schema, generate SQL, execute the query, inspect the result, and then generate the final response.
This means measuring only the total response time is not enough.
Why Cost and Latency Need to Be Evaluated Together
Latency tells you how long the user waits.
Cost tells you how many resources the request consumes.
These metrics can move in different directions.
Consider two queries:
Query | Latency | Data Processed | Result |
|---|
Query A | 2 seconds | 200 MB | Correct |
Query B | 1.5 seconds | 15 GB | Correct |
Query B appears faster, but it may be much more expensive to run.
Now consider another case:
Query | Latency | Data Processed | Result |
|---|
Query C | 12 seconds | 300 MB | Correct |
Query D | 3 seconds | 300 MB | Incorrect |
Query D has better latency but is clearly a worse production outcome.
An evaluation framework therefore needs to consider performance, cost, and correctness together.
Start With a Measurement Model
Before optimizing the agent, define the metrics that matter.
A useful request-level measurement model includes:
Request
|
+-- Total latency
+-- Agent latency
+-- Tool-call latency
+-- BigQuery latency
+-- Bytes processed
+-- Bytes billed
+-- Query count
+-- Retry count
+-- Token usage
+-- Answer quality
Each metric answers a different question.
For example:
Why was the request slow?
-> Latency metrics
Why was the request expensive?
-> Query and data-processing metrics
Why did the agent fail?
-> Error and tool-call metrics
Why was the answer incorrect?
-> Query and evaluation metrics
Measure End-to-End Latency
The first metric to capture is total request latency.
Suppose the application records:
Request started:
10:00:01.000
Response returned:
10:00:05.800
The end-to-end latency is 4.8 seconds.
But that value alone does not tell developers where the time was spent.
Break the request into smaller components.
Total latency: 4.8 sec
Agent processing: 1.2 sec
Schema lookup: 0.4 sec
BigQuery query: 2.7 sec
Response generation: 0.5 sec
This breakdown provides a much clearer optimization target.
If BigQuery takes most of the time, changing the model may not solve the problem.
If agent processing dominates, database tuning may have little effect.
Measure P50, P95, and P99
Average latency is useful for a general overview, but it can hide slow requests.
Suppose an agent processes 1,000 requests:
Average: 3.8 seconds
P50: 2.4 seconds
P95: 8.7 seconds
P99: 15.2 seconds
Most users may have a reasonable experience, while a smaller group experiences much slower responses.
Track at least:
Metric | Meaning |
|---|
P50 | Typical request |
P90 | Higher-latency requests |
P95 | Slow requests affecting a meaningful portion of traffic |
P99 | Extreme latency |
Compare these metrics across different request types.
For example, analytical questions involving large datasets may naturally have higher latency than simple lookup questions.
Track BigQuery Query Count
One user request does not necessarily equal one database query.
An agent may generate:
User request
|
+-- Schema query
+-- Data query
+-- Validation query
+-- Follow-up query
If a request normally requires one query but suddenly starts requiring four or five, the agent behavior may have changed.
Track:
queries_per_request
A simple production metric could look like:
{
"request_id": "req_123",
"query_count": 3,
"total_latency_ms": 6400
}
Unexpected increases in query count are often useful signals for investigating agent regressions.
Measure Bytes Processed
For BigQuery workloads, the amount of data processed is an important performance and cost metric.
For each generated query, capture relevant execution statistics.
For example:
Query ID: bq_7281
Duration: 3.2 seconds
Bytes processed: 2.8 GB
Bytes billed: 3 GB
Status: SUCCESS
Now compare that with another request:
Query ID: bq_7282
Duration: 3.0 seconds
Bytes processed: 420 MB
Bytes billed: 500 MB
Status: SUCCESS
The latency is similar, but the resource consumption is different.
This is why latency should not be used as a proxy for cost.
Track Query Cost
If the application needs financial cost tracking, calculate or retrieve cost information according to the pricing model and configuration used by the BigQuery environment.
Do not hard-code a cost assumption into application logic.
Instead, keep raw usage metrics such as:
bytes_processed
bytes_billed
slot_usage
query_duration
query_count
These values can later be combined with current pricing information in a reporting system.
This also makes historical analysis easier when pricing or infrastructure configuration changes.
Measure Cost Per User Request
A useful business-level metric is the estimated data cost associated with a single agent request.
For example:
Request A
1 query
200 MB processed
Request B
4 queries
8.6 GB processed
Request C
2 queries
1.2 GB processed
The application team can calculate an estimated cost per request based on the actual billing model.
This allows teams to identify expensive request patterns instead of looking only at total monthly usage.
Compare Cost and Latency Together
A useful evaluation table looks like this:
Request Type | P95 Latency | Avg. Bytes Processed | Query Count | Quality |
|---|
Simple lookup | 2.1 sec | 40 MB | 1.1 | High |
Aggregation | 4.8 sec | 900 MB | 1.3 | High |
Complex analysis | 8.9 sec | 6.2 GB | 2.4 | High |
Multi-step analysis | 12.4 sec | 14 GB | 4.1 | Medium |
This provides more useful information than a single "average response time" metric.
It also helps teams decide which request classes need optimization.
Evaluate Query Efficiency
The agent can produce technically valid SQL that is inefficient.
For example:
SELECT *
FROM sales;
may retrieve substantially more data than the application needs.
A more targeted query could select only the required fields:
SELECT
product_id,
sale_date,
revenue
FROM sales
WHERE sale_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY);
The agent evaluation process should therefore consider query efficiency, not just whether the SQL executes successfully.
Useful evaluation signals include:
Columns selected
Filter usage
Partition usage
Bytes processed
Rows returned
Query duration
Query count
Measure Result Size
A query can process a large amount of data but return only a small result.
The opposite can also happen.
Track both:
bytes_processed
result_rows
result_size
For example:
Bytes processed: 12 GB
Rows returned: 25
This may indicate that the agent is performing a broad scan to answer a very small question.
That pattern is worth investigating.
Evaluate Query Correctness
Performance is irrelevant if the generated query answers the wrong question.
Suppose the user asks:
Show total revenue for Europe in 2026.
The generated SQL might accidentally filter by the United States.
The query can execute successfully and return results quickly, but the answer is still wrong.
Evaluation should therefore compare:
User intent
|
v
Generated SQL
|
v
Query result
|
v
Final answer
A successful database execution should not automatically be treated as a successful agent request.
Create an Evaluation Dataset
One practical approach is to maintain a collection of representative questions.
For example:
Question:
What was the total revenue last quarter?
Expected:
Correct date range
Correct metric
Correct aggregation
Correct result
Another example:
Question:
Which product had the highest revenue in Europe?
Expected:
Correct geographic filter
Correct aggregation
Correct ordering
Correct top result
Run these questions against different versions of the agent.
Then compare:
Accuracy
Latency
Query count
Bytes processed
Estimated cost
This creates a repeatable evaluation process.
Compare Agent Versions
Suppose version A generates:
P95 latency: 5.2 seconds
Avg. data scan: 1.8 GB
Accuracy: 92%
Version B generates:
P95 latency: 4.1 seconds
Avg. data scan: 900 MB
Accuracy: 93%
Version B appears to be an improvement across all three dimensions.
Now consider a different result:
Version C
P95 latency: 2.9 seconds
Avg. data scan: 300 MB
Accuracy: 81%
It would be a mistake to deploy Version C simply because it is faster and cheaper.
Agent evaluation should treat answer quality as a core metric rather than an optional metric.
Detect Costly Agent Loops
One of the most important production metrics is repeated tool usage.
For example:
Turn 1
Schema lookup
Query
Turn 2
Schema lookup
Query
Query retry
Turn 3
Schema lookup
Query
Query
Query
If the agent repeatedly performs the same operation, the number of database calls and the total cost can grow quickly.
Track:
tool_calls_per_request
queries_per_request
retry_count
duplicate_query_count
An alert can then identify requests that exceed normal behavior.
Evaluate Follow-Up Questions
Multi-turn conversations require separate evaluation.
Consider:
User:
Show sales by region.
User:
Only show Europe.
User:
Now compare Europe with Asia.
User:
Break that comparison down by product.
Each request depends on the previous context.
Evaluate whether the agent correctly preserves:
Filters
Date ranges
Metrics
Dimensions
Previous results
User intent
A model that performs well on isolated questions may behave differently when questions are chained together.
Build a Production Scorecard
A practical scorecard can combine technical and quality metrics.
Category | Metric |
|---|
Latency | P50/P95/P99 |
Database | Query duration |
Cost | Bytes processed |
Cost | Bytes billed |
Efficiency | Queries per request |
Reliability | Query error rate |
Agent behavior | Tool calls per request |
Context | Token usage |
Quality | Answer accuracy |
Quality | SQL correctness |
User experience | Follow-up success rate |
Do not reduce these metrics to a single score unless there is a clear business reason.
Keeping the dimensions separate makes trade-offs easier to understand.
Common Mistakes
Optimizing only for latency
Reducing latency while increasing query cost can create a more expensive system without providing meaningful user value.
Measuring only successful queries
Failed queries and retries are part of the real production workload and should be included in evaluation.
Treating SQL execution as proof of correctness
A valid SQL query can still answer the wrong question.
Ignoring generated query complexity
An agent can generate increasingly expensive SQL as the prompt or schema changes.
Comparing agents with different test questions
Performance comparisons are meaningful only when versions are evaluated against comparable workloads.
Using only average values
Average latency and average cost can hide expensive outliers. Use distributions and percentile metrics where appropriate.
Best Practices
Define latency, cost, efficiency, and quality metrics before production rollout.
Track BigQuery query statistics for every agent request.
Measure query count rather than assuming one request equals one query.
Evaluate generated SQL for correctness and efficiency.
Maintain a representative evaluation dataset.
Compare agent versions using the same test questions.
Monitor follow-up conversations separately from single-turn requests.
Track retries and repeated tool calls.
Keep raw usage metrics separate from pricing calculations.
Review high-cost and high-latency requests regularly.
Advantages
A structured cost and latency evaluation process helps engineering teams:
Find inefficient generated queries
Reduce unnecessary BigQuery usage
Identify slow request patterns
Detect agent regressions
Compare model or prompt changes
Understand production cost
Improve answer quality
Make deployment decisions using measurable evidence
It also creates a common language between application, data, and AI engineering teams.
Disadvantages and Considerations
Detailed evaluation requires additional telemetry, test datasets, and analysis infrastructure.
There is also no single metric that represents overall agent quality.
A system may be:
Fast but inaccurate
Accurate but expensive
Cheap but slow
Fast and cheap but unreliable
The goal is not to minimize every metric independently.
The goal is to find an acceptable balance between correctness, latency, reliability, and cost for the application's requirements.
Production Evaluation Checklist
Before deploying an AI data agent broadly, verify that you can measure:
End-to-end request latency
P50/P95/P99 latency
BigQuery query latency
Queries per request
Bytes processed
Bytes billed
Query failures
Retry count
Tool calls
Generated SQL
Result size
Answer accuracy
Follow-up accuracy
Estimated request cost
Agent version
Prompt or configuration version
Also make sure the evaluation dataset includes simple questions, complex analytical questions, ambiguous requests, and multi-turn conversations.
Summary
Evaluating an AI data agent requires more than checking whether the final response arrives quickly.
Latency shows how long users wait, while query statistics reveal how much data the agent processes and how efficiently it uses BigQuery. Accuracy and SQL correctness determine whether the system is actually answering the user's question.
A useful production evaluation framework combines end-to-end latency, query latency, bytes processed, query count, tool calls, retries, context usage, and answer quality.
The most important principle is to evaluate the complete request rather than optimizing one metric in isolation. A faster agent is not necessarily better if it produces incorrect answers or generates unnecessarily expensive queries.
By measuring cost, latency, and correctness together, teams can make better decisions about prompt changes, model updates, SQL generation strategies, and production scaling.
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