Production Trace Review Cadence (Retention)

Production Trace Review Cadence (Retention)

1. 01

   Freeze a weekly review window and assign one named owner per client account

   A 45-minute slot every Monday works for most agencies. The owner is accountable for reading traces, not for fixing every issue the same day.

2. 02

   Pull the week's failed and low-scoring traces before the call, not during it

   Filter to sessions where the agent looped, returned an empty response, or scored below the client's agreed quality floor. Langfuse and Arize both expose span-level filters that make this a 10-minute export.

3. 03

   Classify each failure into one of four buckets: retrieval, prompt, tool call, or model drift

   Buckets keep the conversation about root cause instead of anecdotes. A retrieval miss and a hallucinated tool argument need different fixes and different owners.

4. 04

   Tie every recurring failure to a cost or latency number the client already tracks

   A retry loop that adds 4 seconds and two extra model calls per conversation is a budget line, not a technical curiosity. Voker-style dashboards that surface resolution rate alongside spend make this argument concrete.

5. 05

   Log the top three findings in a shared register with a dated owner and a re-test date

   Three items is the ceiling. Longer lists get abandoned by week three, and the register is what proves the retainer is doing work between quarterly reviews.

6. 06

   Re-run the affected evaluation set after each fix and record the before-and-after score

   Confident AI and Braintrust both support saved evaluation sets, so the same test can be replayed against the patched prompt or retrieval config.

7. 07

   Send the client a two-paragraph note: what broke, what changed, what the score moved from and to

   No dashboard link alone. Clients renew on the narrative that their AI got measurably better this month, and the note is the artifact they forward internally.