This page provides troubleshooting suggestions for debugging and observing your agents as you build them.

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View progress through a blueprint

Some blocks such as PDF parsing and text generation can take a while to run. As an agent runs, you can view its progress through the blueprint by navigating to the Blueprint tab. The color of a blueprint block’s border describes the status of its execution.
  • Green: The block completed successfully
  • Animated blue: The block is currently running
  • Red: The block failed
  • No border: The block hasn’t run yet
The following image shows a blueprint with a PDF parsing block that’s currently running. The previous blocks have completed successfully and the following blocks haven’t run yet. Blueprint tab You can also see the progress of the agent in the Log status bar. Learn more below.

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View error and info logs

If there are any errors or messages as your agent runs, you’ll see an indication in the Log bar in the bottom right corner of the page. You can click the Log bar to expand it and see more details. Log bar

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Add additional logs with Log Message blocks

You can add additional custom Log message blocks to the agent for debugging purposes. Log messages are helpful to understand the flow of the agent and the value of state and other variables at a given point in the execution. To add a log message, add a Log message block to the canvas. In the block’s configuration panel, update the following fields:
  • Type: info or error
  • Message: The message to log
Below is an example of a log message block during a file parsing process. It logs the file ID before beginning the parsing process, to help you debug if the file isn’t found or isn’t parsed correctly. Log message block When the block runs, you’ll see the log message in the Logs bar at the bottom of the page. Log message in logs

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Add additional logs with Python code

You can use python print statements and the globally available logger object to add additional logs to the agent. 
print("This is a log message that shows in logs as 'Captured stdout'")
logger.info("This is an info message that shows in logs under 'Captured logs'")
logger.warning("This is a warning message that shows in logs under 'Captured logs'")
logger.error("This is an error message that shows in logs under 'Captured logs'")
The following image shows an example of a Python block that logs messages via print statements and the logger object. Python block with logs When the block runs, the log messages appear in the Logs bar at the bottom of the page. Log messages in logs

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View traces

The log bar shows information about the agent’s execution as it runs and after blocks complete. It includes the following:
  • The block name
  • The status of the block: success, error, or in progress
  • A link to view a trace at that point in the execution
  • How long the block took to run
Log bar with traces The trace link opens a new tab with a detailed view of the agent’s execution at that point in time. It contains:
  • The value resulting from the block’s execution, which is then added to the execution environment of the following block
  • The return value, if the block has one
  • The full execution environment at that point in time
  • The call stack
Below is an example of a trace after a File upload block has completed. Trace of a file upload block

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Inspect agent state

You can use the State explorer to view an agent’s state variables and their values. This is helpful when you’re debugging an agent or need to check the state at a given point in the execution. To access the state explorer, click the State explorer icon in the top right corner of the page. State explorer

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Observe agent usage and performance

You can view usage and performance metrics for your agent in the Observability tab. To get to this view, select the agent from the AI Studio homepage and navigate to the Observability tab. Here, you can view:
  • Performance: Requests, errors, latency, and throughput
  • Usage: Total requests, tokens, and cost, along with a geographic breakdown of requests
  • Logs: Logs from individual requests