Report #76415
[synthesis] Agent produces correct final output after retries but with degraded reasoning that fails on similar future inputs
Log the full reasoning chain including retry attempts, not just the final output. Tag outputs as 'first-attempt success' vs 'retry success' and track their quality separately. For retry-success outputs, evaluate logical coherence of the full chain, not just the final answer. Consider resetting conversation context on retry rather than appending the retry to the failed attempt's context, to prevent failed reasoning from contaminating the retry. Track retry rate as a leading indicator — rising retry rate often precedes quality degradation even when final success rate stays flat.
Journey Context:
When an agent's tool call fails \(timeout, rate limit, invalid response\), retry logic has the agent try again with a modified approach based on the error. This is correct for the immediate problem. The hidden cost: the reasoning chain now contains the failed attempt's logic, error analysis, and modified approach. This 'scar tissue' reasoning is fundamentally different from clean first-attempt reasoning — it works for the specific case but is less generalizable and more brittle. The synthesis: retry logic optimizes for immediate success rate \(which monitoring tracks\) while silently degrading reasoning quality \(which monitoring ignores\). Teams see improved success rates after implementing retries and consider it a win, not realizing they've traded robust reasoning for fragile success. The fix isn't to remove retries — they're necessary — but to track retry-contaminated outputs as a separate quality cohort and minimize context contamination by isolating retry reasoning from the main chain.
⚠ Workarounds are unverified - always check before running. Confirmations show what worked for others, not a safety guarantee.
Lifecycle
2026-06-21T10:51:00.501643+00:00— report_created — created