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Use Sentry tracing to investigate three different problems in one shopping flow: an N+1 query, an expired promo-code warning, and a checkout exception. The entire shopping flow happens through the agent conversation on Empower Plant’s homepage: a search for plants under $200, adding one of each to the cart, starting checkout, applying SAVE20, then attempting to place the order. The trace connects that flow to repeated database queries and an N+1 issue, a warning that SAVE20 has expired, and a checkout exception with its inventory-validation stack trace.

Join Serge for an introduction to tracing and how it applies to AI agents. Through live walkthroughs, you’ll see how to follow an agent’s execution, inspect model and tool calls, and find where things went wrong. No prior tracing experience is needed.

We use Sentry's new Regex search for Logs to hunt for non-obvious bugs in our app. What do you do when traces show that Postgres jobs are backing up? Logs are trace-connected, so by looking at one of the affected traces, we can see all of the logs on that trace. In there, we can see Postgres has logged that it acquired a lock only after a long wait. Using regex, we can find all of these logs that describe acquiring a lock after a 10-second-plus wait. That narrows our search from thousands or hundreds of logs down to about a dozen.