The article discusses how successful retries in AI agent traces can complicate analysis due to uncertainty in identifying similar calls, and introduces Traser, a tool to highlight meaningful differences and reduce uncertainty for engineers.
been thinking about something that came up in the last thread i posted here say an agent calls a tool gets something wrong retries a couple times then eventually succeeds at first glance looks easy enough to analyze right? group the retries together compare them figure out what changed but theres a nasty problem here how do u actually know 2 similar calls are attempts at the same logical operation? same tool + nearly identical args doesnt prove it could be: an actual retry the same tool being called again for a different reason a different branch reaching a similar operation state changing between attempts and making what looks like a retry meaningfully different someone in the last thread made a point i liked: similarity is useful for narrowing candidates, but similarity shouldnt magically become identity if the trace has a correlation id attempt id same parent span idempotency key same row being touched etc then u have stronger evidence if none of that exists im starting to think the right answer is just admitting the pairing is uncertain instead of pretending a 0.92 similarity score makes it true the other thing im still thinking through is first divergence vs useful divergence the first thing that changed might be technically real but completely useless to the engineer timestamp changed. request id changed some harmless metadata moved meanwhile the thing that actually explains the weird behavior might happen 8 steps later but the opposite problem exists too. once u start ranking every possible difference, its pretty easy to build a fancy noise generator so the thing im trying to get right with Traser is less: find every difference and more: show me the few differences that are actually worth an engineer looking at tell me why they surfaced and dont hide uncertainty curious how people here handle this today if ur traces dont have clean retry/correlation ids, how are u deciding 2 calls belong to the same operation? do u usually care more about the first divergence or the first one that actually changes downstream behavior? and when u build heuristics around this, what makes u trust them enough to act on them? also im looking for a few more teams to be design partners on Traser and help me shape this around real usage some more(its free lol). if ur running agents in prod and have ugly traces from somereal failures, lmk. dont care if the run makes Traser look smart or completely stupid, both are useful. otherwise if ur a solo dev just wanting to save some time ill post the link below feel free to let me know what we can improve on
This article discusses how retries in AI systems, particularly with LLMs and agents, can exacerbate failures when underlying issues are not addressed, leading to repeated mistakes with increased cost and latency.
The article discusses how AI agents often fail silently by completing tasks incorrectly without crashing, leading to undetected errors. It highlights common failure modes and explores potential detection strategies.
Advocates for treating agent failures as evaluation benchmarks rather than just trace logs, emphasizing the need for systematic testing of AI agent behaviors.
A developer builds a debugging tool for AI agents that compares replays against reference runs to identify where behavior first drifted, expressing frustration with manual trace debugging.
The article discusses strategies for capping retries in AI agents to balance cost and performance, emphasizing the need to differentiate between retryable errors and cases requiring escalation to more capable models in production.