AI agent tracing

By @edhonour · Instagram

Source: https://www.instagram.com/reel/DdNmCoBpqSm/?stkn=YWtzOHNkdWYzeGhx

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Summary

The speaker argues that anyone building AI agents or workflows needs to trace every prompt, setting, and output from day one. Without trace logs you can't tell if changes to your system help or hurt performance, and those logs need to stay secure since they often contain sensitive prompt data.

Core ideas

One context card plus 6 ideas

Tracing as visibility: Without trace logs, you have no real insight into what an agent or workflow is doing behind the scenes.
What to log: Track incoming prompts, the system prompt, all API settings, and the resulting output together as a set.
Evaluation depends on history: Any upgrade decision needs trace data to prove whether a change actually improved performance or made it worse.
Blind changes are risky: Modifying an agent without trace history means you're guessing rather than measuring.
Security matters: Trace files often contain prompt content you want kept private, so they need to be secured, not left exposed.
Tracing applies broadly: This isn't a niche practice, it should be built into every AI application, not just complex multi-agent pipelines.

Quotes

“Without having these trace logs, you're really blind to what the agent or the workflow is doing.”
@edhonour

“It is really important to integrate tracing into all of your applications.”
@edhonour

“Make sure your trace files are kept secure because there is going to be information in your prompts that you are going to want to keep to yourself.”
@edhonour

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