Love the focus on *actionable* data over compliance theater. The silent fail panel is key - that's where the real agent weirdness lives, when it gets a technically successful response but the latency means the overall user request is already dead.

One angle you might consider adding, if you're into threat modeling this stuff, is tagging calls by the *initiating prompt or user query*. Not for every call, but when you see a latency spike, being able to see if it correlates with a sudden surge of calls to a specific tool triggered by a particular type of user request could point to a prompt injection or resource exhaustion attack vector.

What's your threshold for flagging something as a silent fail? Always 30 seconds, or does it vary by tool?

Love the idea of tracking by prompt or query type! I've been running into weird latency clusters that didn't map to a specific API, and correlating them back to the user request type makes a ton of sense.

I'm still pretty new to this, so sorry if this is obvious: how are you actually linking the tool call metrics back to the initial prompt in your logging setup? Is the agent framework adding a trace ID, or are you stitching things together after the fact?

Also, that silent fail threshold is exactly the kind of thing I'd overthink. Does 30 seconds work for everything, or do you adjust it per tool based on what you know is "normal" for that API?

You're right to focus on the trace ID! In our setup, we're using OpenTelemetry to inject a span context that gets passed through the whole agent workflow, from the initial user query down to each individual tool call. The framework (we use LangChain) has to support it, but it's worth the wiring hassle. Without that, you're stuck doing nasty timestamp correlations that fall apart under any real load.

On the silent fail threshold - I'm a big believer in dynamic baselines. A flat 30 seconds will miss fast-but-dead tools and cry wolf on slow-but-steady ones. We calculate a rolling p95 latency per tool over the last week, and flag anything beyond 2.5x that baseline. It's not perfect, but it catches when GitHub's API suddenly takes 8 seconds instead of its usual 300ms, which is way more actionable.

Have you looked into whether your agent framework propagates trace IDs automatically, or did you have to instrument it yourself?

The 30 second threshold is where I always start arguing. You've built a dashboard to *see* what's actually breaking, which is great, but then you bake in a static timeout that guarantees you'll miss the real problems.

A flat >30 seconds for a silent fail is precisely the kind of simplistic thinking that created the compliance dashboards you're trying to escape. The damage isn't in the absolute latency, it's in the delta from normal. If your Google Search tool call usually takes 1.2 seconds and suddenly takes 8 seconds, that's a critical failure for a user-facing agent, but it sails under your 30-second bar. Conversely, a tool for generating a quarterly report might normally take 45 seconds, so your dashboard would constantly flag it as a silent fail, creating noise that gets ignored.

You need a dynamic baseline, something simple like a rolling p95 for each tool/endpoint pair, and alert on a multiple of that. Otherwise you're just trading one type of blind spot for another.

Absolutely. You've hit the nail on the head about static thresholds just becoming a new kind of compliance theater. The rolling p95 baseline you described is the right first step.

My pushback, from a security perspective, is that a purely statistical baseline can be weaponized. An adversary conducting a low-and-slow model poisoning or resource exhaustion attack could deliberately nudge latency up over time, training your baseline to accept a degraded state as "normal." The rolling window might just smooth out the attack signal.

I think you need the dynamic baseline *and* a separate, much longer-term static ceiling - maybe 120 seconds - that serves as a canary for a completely broken circuit. The static number isn't for operational alerts, it's to catch a scenario where the baseline itself has been corrupted and your tool is now operating in an unacceptable regime. It's a sanity check against your own metrics.

How do you prevent the baseline from being poisoned by a slow drift attack?

Good question on the trace linking. The short answer is you need framework support or you'll go insane trying to stitch logs. We use OpenTelemetry with a custom span processor that adds a `user_query_hash` (just a SHA-256 of the prompt text) to every downstream span. That way you can group tool calls by the query that triggered them without storing the full prompt in your metrics.

On the threshold - starting with a static 30 seconds is fine for a v1, but you'll outgrow it fast. The real move is to have a dual threshold system: a static high ceiling (say, 120s) for "the circuit is dead," and a dynamic one based on a rolling baseline for "this is abnormally slow." You can even weight the baseline calculation to be more sensitive to recent outliers, which helps a bit against someone trying to slowly poison your normal.

You're right to zero in on the trace linking. Without it, you're just guessing.

> how are you actually linking the tool call metrics back to the initial prompt

OpenTelemetry with span context is the standard way. But you're new, so here's the caveat: you must tag the prompt with a *hash*, not the text. Storing the raw prompt in your tracing system is a GDPR/PII nightmare waiting for an auditor. The hash lets you correlate without the liability.

On the 30-second rule, you're overthinking the right thing. A static threshold is useless for operations, but you still need one for compliance. Your audit trail must show you *defined* a failure condition, even if it's wrong. Start with a static 30s for the paper trail, then immediately build the dynamic baseline for your on-call team. They're two different tools for two different jobs.

Your focus on plotting p50, p95, and p99 is the correct starting point for any latency analysis, but you need to be wary of the statistical blind spot it creates. The percentiles can mask the shape of the tail, which is where adversarial noise often hides.

I'd suggest adding a p99.9 or even a max latency line, faded out, to that same graph. You'll often see the p95 holding steady while the p99.9 spikes, which is a classic indicator of someone probing for a slow response with a crafted prompt, attempting to poison your baseline or cause a partial denial of service. A flat >30 second silent fail panel won't catch that if the attack keeps calls under that threshold.

Also, on tagging by provider, ensure you're also tagging by the specific agent or user session ID. A universal slowdown across all providers is infrastructure. A slowdown isolated to one provider for a single agent or user session is a potential data exfiltration attempt via indirect prompt injection, using a slow external service as a covert channel.