Debug Streaming Agents with Confluent Cloud

Streaming Agents provide debugging capabilities that give you visibility into agent behavior. This guide explains how to use these features to build, test, and troubleshoot intelligent streaming workflows.

Debugging a streaming agent workflow, in which agents and tools are defined with the CREATE TOOL and CREATE AGENT statements and orchestrated by AI_RUN_AGENT, requires a blend of observability practices, trace inspection, and output validation. This guide describes key techniques for debugging and troubleshooting these workflows systematically.

Leverage built-in tracing and auditing

Automatic trace logging

Every invocation of AI_RUN_AGENT and tool calls, including UDFs and external MCP services, generates system traces. These traces log the agent name, input payload, selected tools, tool outputs, and timing.

Inputs and outputs are event-driven, so you can always replay an agent’s actions, because they’re backed by a Apache Kafka® topic. Agent traces provide the interaction history, which is a trace of all interactions that are logged automatically in a Kafka topic.

Trace auditing

Access trace logs through Flink’s logging system or integrated monitoring tools. Look for agent session information, invocation order, response times, and any error events.

What to check

Did the agent receive the correct input?
Which tools did the agent select and invoke for each input row?
Did tool outputs match expectations for type, format, and content?
Were any calls delayed, retried, or failed?

Inspect output tables for workflow verification

Output table monitoring

Streaming agent results are inserted into output tables specified in CREATE AGENT. Regularly query or export these tables to validate correctness and completeness.

Schema and content verification

Ensure the output table schema matches agent definitions and tool outputs. Look for missing fields, malformed data, or unexpected results, which are indicators of context or agent prompt issues.

The following statement is an example diagnostic query.

SELECT *
FROM processed_claims
WHERE agent_result IS NULL
   OR agent_result LIKE '%error%'
LIMIT 100;

Enable and check application logs

Flink job logs

Use Flink’s job manager/task manager logs to catch runtime errors, exceptions, warnings, or unexpected agent/tool behavior. Look for stack traces, input/output dumps, and system messages.

Custom debug logging

If you’re using the Table API or custom UDFs, integrate custom logging statements inside your function code to track variables and conditions. For more information, see Enable Logging in a User Defined Function.

Replay and isolate problematic events

Replayability

Because the system audits every event and agent/tool call, you can replay input events that led to errors or suspicious outcomes by resubmitting these records into the workflow or testing them in isolation.

Step-by-step isolation

Temporarily limit the agent’s tool array to a single tool or simplify the system prompt to narrow down issues in orchestration logic.

Tune agent prompts and tool definitions

Prompt adjustment

Many workflow errors trace back to ambiguous or overly complex system prompts. Refine prompts to make decision logic explicit and reference tool names directly.

Tool validation

Verify that tool registration with CREATE TOOL correctly lists endpoint/UDF names, access parameters, and descriptions. Unused or misnamed tools may go uncalled or produce unexpected errors.

Monitor tool invocation metrics

Performance metrics

Use Flink’s streaming metrics and built-in auditing to monitor latency, throughput, and error rates for each tool call.

Error-rate analysis

Regularly review metrics dashboards or log summaries to detect patterns, such as higher error rates or latency spikes, which often indicate workflow bottlenecks.

Best practices

Replay strategy

Start with small, representative datasets

Test edge cases and error conditions

Compare multiple agent versions systematically

Document replay results and decisions

Debugging workflow

Analyze step-by-step execution

Identify root causes of issues

Test fixes with replay

Deploy and monitor

Performance optimization

Monitor tool call latencies

Identify frequently used tools

Optimize tool implementations

Cache frequently accessed data

Use appropriate timeouts

Security and privacy

Sanitize sensitive data in logs

Control access to replay data

Implement data retention policies

Monitor for data leaks

Troubleshooting

Replay failing

Verify session data exists
Check agent version compatibility
Ensure all dependencies are available
Check for data corruption

Performance issues during replay

Use smaller session sets
Optimize queries
Check resource availability
Consider parallel replay

Debugging tips

Use step-by-step analysis to understand agent behavior
Compare successful and failed sessions
Look for patterns in tool call failures
Monitor resource usage during replay
Use visualization tools for complex workflows