Agent traces explain behavior while operating reviews demand evidence of outcomes.
AI application engineers need diagnostic detail, while product and finance leaders need a shared measure of value.
Who you’d be doing this for
“My traces show every tool call, but Monday’s review still starts with, ‘Did this actually help anyone?”
Manuela Lima · AI Application Engineer
Operates a customer-facing support agent and has to explain its quality and operating cost to product and finance leaders.
What is at stake
Design partners can inspect agent behavior but cannot connect it to whether customers completed the intended task. You must weigh a useful shared outcome signal against a first release that stays narrow and verifiable.
Why it isn’t already fixed
Every obvious fix costs something else. That’s the part you’d have to decide.
- trace detail vs outcome clarity
- flexible customer definitions vs comparable signals
- pilot speed vs measurement trust
- product boundaries vs integrated investigation
Why Datadog
Datadog Agent Observability already connects agent traces and evaluations with production telemetry, making outcome evidence the missing link for AI application teams.
Written with these in mind
Not your kind of problem? 40 more at Datadog, or browse every organization.
This is the setup. The work is inside.
Running it puts you in the room: the full situation and its constraints, stakeholders who push back in their own words, and the decisions that are yours to make. What you produce becomes a Day One Plan — work you can show someone instead of describing.