Agent traces unlock faster iteration and better evaluations
Original: the importance of traces!
Source: x.com ↗
Who: Posted by @hwchase17 (Harrison Chase, co-founder of ), quoting Adam Lucek, who works on production .
What's new: The argument here is that is one of the most valuable assets a team building AI agents can collect. The advice is to ship your agent early, watch what it actually does in the real world, and use those recordings to make it better fast.
How it works: Every time an agent runs, a trace captures the full chain of events: the original input, each intermediate step, every tool the agent called, and the final output. Teams then analyze those traces in two ways. First, they read them to spot where the agent gets confused or wastes steps. Second, and more powerfully, they feed traces into — automated checks that score whether the agent is behaving correctly, so future changes can be tested before going live.
Why it matters: Most AI agent failures are invisible until you look at the logs. Trace-driven iteration is the difference between guessing why your agent is broken and knowing exactly where it went wrong, which makes the improvement loop much faster and more reliable.