AI-powered data agents are becoming more useful for applications that need to answer questions from large datasets. Instead of requiring users to write SQL queries or navigate dashboards, a data agent can interpret a question, generate a query, execute it, inspect the result, and continue the conversation when the user asks a follow-up question.
This creates a different monitoring problem from a traditional API or database application.
A single user conversation can contain several turns, multiple generated SQL queries, retries, tool calls, and changes in context. If monitoring only records the final response, it becomes difficult to understand where latency, cost, or incorrect answers originated.
For applications using BigQuery as the data layer, monitoring the complete conversation provides a much better view of agent behavior.
What Is a Multi-Turn Data Agent Conversation?
A multi-turn conversation happens when a user asks an initial question and then continues with related questions.
For example:
User:
Show me monthly sales for the last 12 months.
Agent:
Here are the monthly sales results.
User:
Which month had the highest sales?
Agent:
December had the highest sales.
User:
Now compare that month with the same month last year.
Agent:
December sales increased by 14% year over year.
Although this looks like one conversation to the user, the application may perform several independent operations behind the scenes.
A typical request could involve:
User message
|
v
Agent reasoning
|
v
Tool selection
|
v
SQL generation
|
v
BigQuery query
|
v
Result processing
|
v
Agent response
The next user message can reuse information from the previous turns.
That means monitoring needs to connect all of these operations to the same conversation and user request flow.
Why Conversation-Level Monitoring Matters
Traditional application monitoring often focuses on individual requests.
For example, an API monitoring system might record:
Request ID
Response time
HTTP status
Server errors
That is useful, but it does not provide enough information for an AI data agent.
Consider a conversation with six user messages. The first request takes two seconds, while the fifth request takes 18 seconds.
The application team needs to determine whether the additional latency came from:
- A larger SQL query
- More rows returned from BigQuery
- Additional agent reasoning
- Multiple tool calls
- A failed query followed by a retry
- Increasing conversation context
- A query generated from an inaccurate interpretation
Conversation-level telemetry makes these relationships visible.
Create a Conversation ID
The first step is to assign a stable identifier to every conversation.
For example:
conversation_id = conv_8f31a2
Every user turn and every tool operation belonging to that conversation should carry the same identifier.
A simplified request model in C# might look like this:
public sealed class AgentRequest
{
public string ConversationId { get; init; } = default!;
public string UserMessage { get; init; } = default!;
}
When the conversation starts, the application creates the ID.
For subsequent requests, the existing ID is reused.
This allows monitoring systems to group events such as:
Conversation Started
|
+-- Turn 1
| +-- SQL Query
| +-- BigQuery Result
|
+-- Turn 2
| +-- SQL Query
| +-- BigQuery Result
|
+-- Turn 3
+-- SQL Query
+-- Retry
+-- BigQuery Result
Without this relationship, these events may appear to be unrelated requests.
Track a Separate Turn ID
A conversation ID identifies the complete conversation, but each user interaction should also have its own turn ID.
For example:
conversation_id = conv_8f31a2
turn_id = turn_003
This distinction becomes important when a single conversation contains multiple questions.
A useful telemetry structure is:
Conversation ID
Turn ID
User request
Start time
End time
Agent latency
Tool calls
BigQuery queries
Query duration
Bytes processed
Errors
Final response
The conversation ID provides the long-term context, while the turn ID helps investigate one specific interaction.
Measure End-to-End Turn Latency
For every turn, record the complete processing time.
For example:
Turn 1: 2.4 seconds
Turn 2: 3.1 seconds
Turn 3: 8.7 seconds
Turn 4: 4.0 seconds
The average alone may hide important problems.
Suppose the average latency is four seconds, but some requests take 20 seconds. Users experiencing those slower requests will still notice the problem.
Use percentiles when analyzing latency.
| Metric |
What it shows |
| P50 |
Typical user experience |
| P75 |
Higher-than-normal latency |
| P95 |
Slow requests that affect a meaningful portion of users |
| P99 |
Extreme latency cases |
Tracking these values separately for each conversation turn makes performance problems easier to identify.
Monitor BigQuery Query Latency
The agent's response time and the database query time are not the same thing.
For example:
Total turn latency: 7.8 seconds
Agent processing: 2.1 seconds
BigQuery query: 5.2 seconds
Response formatting: 0.5 seconds
This immediately tells the development team that BigQuery execution is responsible for most of the delay.
Store the query duration as a separate telemetry field.
A simplified record might look like:
{
"conversation_id": "conv_8f31a2",
"turn_id": "turn_003",
"query_duration_ms": 5200,
"total_turn_duration_ms": 7800
}
This separation is useful when deciding whether a performance problem belongs in the agent layer or the data layer.
Track Bytes Processed and Query Cost
Latency is only one side of the problem.
An AI agent can also generate queries that scan more data than expected.
For example:
Turn 1:
Bytes processed = 120 MB
Turn 2:
Bytes processed = 2.4 GB
Turn 3:
Bytes processed = 18 GB
The third request deserves investigation even if it returns the correct answer.
Record query-level usage alongside conversation information:
conversation_id
turn_id
query_id
bytes_processed
bytes_billed
query_duration
query_status
This allows teams to analyze database consumption by conversation, user, feature, or application workflow.
It is particularly useful when an agent is allowed to generate SQL dynamically.
Monitor Generated SQL Carefully
The SQL generated by an agent is one of the most useful debugging signals.
For example, a user might ask:
Show revenue by product for the current quarter.
The agent may generate:
SELECT
product_id,
SUM(revenue) AS total_revenue
FROM sales
WHERE sale_date >= DATE_TRUNC(CURRENT_DATE(), QUARTER)
GROUP BY product_id;
If the result is incorrect, developers need to inspect the query that produced it.
However, storing raw SQL without considering security and privacy can create another problem.
Avoid putting sensitive values, credentials, personal information, or unrestricted user data into application logs.
Where appropriate, store:
- Query fingerprints
- Sanitized SQL
- Query metadata
- Table names
- Execution statistics
- Error information
rather than unrestricted raw request content.
Track Tool Calls Per Turn
A multi-turn agent may perform more than one tool call.
For example:
Turn 4
|
+-- Schema lookup
|
+-- SQL generation
|
+-- BigQuery query
|
+-- Result validation
|
+-- Second BigQuery query
|
+-- Final response
If monitoring only records the final BigQuery query, the application team may miss the reason why the turn became slow.
Track tool-call count and duration.
{
"conversation_id": "conv_8f31a2",
"turn_id": "turn_004",
"tool_calls": 5,
"bigquery_queries": 2,
"total_duration_ms": 9100
}
This also helps detect agent loops.
Detect Repeated Tool Calls and Agent Loops
An agent can occasionally repeat an operation.
For example:
Query 1 -> Error
Query 2 -> Error
Query 3 -> Error
Query 4 -> Error
Or it may repeatedly request the same schema information.
A simple monitoring rule can flag turns with an unusually high number of tool calls.
For example:
if tool_calls > expected_threshold:
flag_for_review()
The threshold should be based on the application's normal behavior rather than an arbitrary global number.
Repeated tool calls can increase:
- Latency
- BigQuery usage
- Token consumption
- Infrastructure cost
- User frustration
Monitor Conversation Context Growth
Multi-turn conversations naturally accumulate context.
Consider:
Turn 1: 1,000 tokens
Turn 2: 2,000 tokens
Turn 3: 4,000 tokens
Turn 4: 7,000 tokens
Turn 5: 11,000 tokens
If the application sends the complete conversation history to the model for every request, later turns can become more expensive and slower.
Monitor context-related metrics alongside database metrics.
Useful fields include:
input_tokens
output_tokens
conversation_turn_count
context_size
tool_call_count
This makes it easier to determine whether increasing latency is caused by BigQuery or by the growing agent context.
Use Correlation IDs Across the Entire Stack
Conversation ID alone may not be enough for distributed systems.
A better telemetry model can use:
conversation_id
turn_id
request_id
query_id
trace_id
For example:
conversation_id: conv_8f31a2
turn_id: turn_004
request_id: req_91af
query_id: bq_74d1
trace_id: trace_00192
These identifiers can connect application logs, agent execution traces, database operations, and error reports.
When a production incident occurs, engineers can start with the conversation ID and follow the request through the entire system.
Build a Conversation-Level Dashboard
A useful dashboard should not show only total query volume.
Include metrics such as:
| Metric |
Purpose |
| Active conversations |
Shows current usage |
| Turns per conversation |
Identifies conversation depth |
| P50/P95 turn latency |
Measures user experience |
| P50/P95 query latency |
Measures BigQuery performance |
| Bytes processed |
Tracks data scanned |
| Query error rate |
Identifies database failures |
| Tool calls per turn |
Detects complex agent behavior |
| Retry rate |
Identifies unstable operations |
| Token usage |
Tracks model consumption |
| Cost per conversation |
Connects usage to spend |
The dashboard should also allow filtering by application version, environment, user segment, and date range.
Compare First Turns and Follow-Up Turns
Initial requests and follow-up requests often behave differently.
For example:
Initial turn:
User asks a broad question.
Follow-up:
"Now show only Europe."
Follow-up:
"Compare that with last year."
Follow-up:
"Which product changed the most?"
Later questions may depend heavily on previous results.
Monitor these groups separately.
You might discover:
Initial turn P95: 4.2 seconds
Follow-up P95: 6.8 seconds
Third-turn P95: 9.1 seconds
That pattern can indicate growing context, more complicated queries, or repeated agent operations.
Common Monitoring Mistakes
Monitoring only the final answer
The final response does not explain how the agent reached it.
Record the important execution stages behind the response.
Tracking database latency without conversation context
A slow query is easier to understand when you know which user turn generated it.
Logging everything without filtering sensitive data
AI applications can process large amounts of user and business information. Logging every prompt, SQL statement, and result can create security and compliance risks.
Using only average latency
Average latency can hide slow requests. Use percentile measurements.
Ignoring follow-up questions
Multi-turn behavior is one of the defining characteristics of conversational agents. Monitoring only the first request gives an incomplete picture.
Best Practices
- Generate a stable conversation ID when a conversation begins.
- Assign a unique turn ID to every user interaction.
- Connect agent operations and BigQuery queries using trace or correlation IDs.
- Track latency separately for agent execution, tool calls, and BigQuery queries.
- Monitor bytes processed and query cost in addition to execution time.
- Track tool-call counts to detect repeated operations and agent loops.
- Monitor context growth across conversation turns.
- Sanitize sensitive information before writing telemetry to logs.
- Compare first-turn and follow-up performance.
- Create alerts around unusual latency, query volume, retries, and data scanned.
Advantages
Conversation-level monitoring provides a clearer view of how an AI data application behaves in production.
It helps teams:
- Debug incorrect answers
- Identify expensive queries
- Find slow conversation turns
- Detect repeated tool calls
- Understand context growth
- Investigate database failures
- Compare agent versions
- Control operational costs
The main advantage is that the team can investigate the complete path from a user's question to the final response.
Disadvantages and Considerations
Detailed observability also introduces additional engineering work.
Telemetry can increase:
- Storage requirements
- Logging volume
- Monitoring costs
- Instrumentation complexity
- Data governance requirements
There is also a risk of collecting sensitive information if telemetry is designed without proper controls.
For this reason, observability should be designed together with security and data-retention policies.
Production Monitoring Checklist
Before running a BigQuery data agent at scale, verify that your monitoring system can answer these questions:
Can we identify every conversation?
Can we identify every turn?
Can we trace each BigQuery query back to its turn?
Can we measure P95 turn latency?
Can we measure BigQuery query latency separately?
Can we identify expensive queries?
Can we detect repeated tool calls?
Can we detect retries and failures?
Can we measure conversation context growth?
Can we investigate an incorrect answer from its generated SQL?
Can we remove sensitive information from telemetry?
Can we compare agent behavior between application versions?
If several of these questions cannot be answered, the application's observability layer probably needs additional instrumentation.
Summary
Monitoring a multi-turn BigQuery data agent requires more than tracking API response times and database errors.
Each conversation should be treated as a sequence of related events. Conversation IDs connect the overall session, turn IDs identify individual user interactions, and trace or query IDs connect agent operations with BigQuery execution.
The most useful monitoring approach combines conversation latency, tool-call behavior, generated query information, bytes processed, query failures, context growth, and cost.
With this model, production teams can move beyond simply knowing that an agent is slow or expensive. They can identify which conversation turn caused the problem, which operation consumed the time or resources, and what needs to be changed.
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