Let’s say your team shipped a support agent last quarter. The launch demo went well, stakeholders were pleased, and everyone moved on. A few months later, things start to look off. Summaries of long conversations are truncated, and monitors show latency spikes on tool calls to the billing API. Your team’s first instinct is to ship fixes such as tweaking prompts or upgrading the model. After the updates, performance seems to improve, but you still can’t tell why a change helped or whether it will hold as traffic changes.
The problem isn’t a lack of telemetry data. Teams that build and ship agentic systems usually capture more trace data than they can possibly review, but don’t have a repeatable way to identify where an agent is underperforming and measure whether a change improves the intended outcome.
In this post, we’ll cover how to read your agent traces as a roadmap for where to invest, why teams should run both evaluations and experiments, and how to bring them together into an optimization loop.
Use agent traces as a roadmap
Teams often look into traces when they need to debug bad interactions. Analyzing trace data in aggregate can reveal recurring patterns and show you where to invest next. When traces are connected to evaluation scores and outcomes, they can narrow vague concerns like “the agent could be better” into claims that are specific and testable, such as “our summarization prompt underperforms on threads over 15 messages, and those tickets reopen at twice the normal rate.”
