This hit close to home because most teams spend a lot of time thinking about model quality and almost no time thinking about failure economics.

The interesting part for me wasn't the retry loop itself it's that the loop often isn't caused by a catastrophic bug. Sometimes it's just a parser mismatch, a bad tool response, or an agent getting stuck trying to be helpful. The failure is small, but the cost compounds silently.

I also like that the solution sits at the gateway layer instead of inside a specific framework. In practice, teams end up with a mix of LangChain, custom agents, background workers, and experiments. Having one place that can spot repetitive behavior across all of them feels much more reliable than hoping every developer remembers to set the right iteration limits.

One thing I've started treating as seriously as latency and error rates is "cost anomalies per workflow." A workflow completing successfully doesn't necessarily mean it's healthy if it took 20x the expected number of model calls to get there.

Good reminder that agent observability isn't just about correctness anymore it's also about preventing small failures from turning into surprisingly expensive ones.