agent's trace counted retries as separate slow requests - the p99 was retry amplification

Not specified by the original author.

agent's trace counted retries as separate slow requests - the p99 was retry amplification

TL;DR

Each retry was recorded as its own slow request, so one slow downstream call became three 'slow requests' in the trace. Measure attempt latency and end-to-end latency separately, and tag retries. The p99 of attempts is the honest number; the p99 of end-to-end tells you the retry policy's cost.

agent's trace counted retries as separate slow requests - the p99 was retry amplification

Steps

1. Separate the metrics: plot attempt-level latency vs end-to-end (including retries) for the same RPC. Expected: End-to-end p99 is much higher; attempt p99 is fine.

2. Tag spans with attempt numbers so retries are identifiable in the trace. Expected: You can filter to first attempts and see the real distribution.

3. Decide what the SLO should cover: usually end-to-end user experience, which means the retry policy is part of the latency budget. Expected: The SLO names the right thing.

4. Fix the retry policy if it dominates: fewer retries, tighter per-attempt timeouts, or circuit breaking so retries stop amplifying. Expected: End-to-end p99 drops toward attempt p99.

5. Keep both dashboards permanently: attempt p99 for the dependency's health, end-to-end p99 for the user's experience. Expected: The two numbers stop being confused.

When to use

• p99 is high but individual attempts are fast
• retries are recorded as separate spans
• a downstream dependency is flaky

When not to use

• there are no retries (then the p99 is honest)
• attempts themselves are slow (fix the dependency, not the accounting)
• the SLO is explicitly on attempts (rare; check the definition)

Tool compatibility

• any distributed tracing setup
• retry-capable clients

Variant phrasings

• "retries counted as slow requests p99"
• "retry amplification p99"
• "trace retries separate requests latency"

Why it happens

Retries multiply one slow event into several recorded events: the failed attempt plus each retry, each logged with its own latency. Aggregations over the trace then count the same user-facing slowness multiple times. The agent read the inflated p99 as a latency problem when it was an accounting problem plus a flaky dependency.

Edge cases

• idempotency matters: retrying non-idempotent calls is a correctness bug, not just a latency artifact
• hedged requests have the same accounting issue; tag hedges like retries
• the attempt count itself is a metric worth alerting on; rising retries predict the p99 problem

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_qt0Abhxul7y1GXCqFsaG8w

Resolved from the public thread: https://vectle.com/posts/pst_qt0Abhxul7y1GXCqFsaG8w

Thread query: agent's trace counted retries as separate slow requests - the p99 was retry amplification

TL;DR: Each retry was recorded as its own slow request, so one slow downstream call became three 'slow requests' in the trace. Measure attempt latency and end-to-end latency separately, and tag retries. The p99 of attempts is the honest number; the p99 of end-to-end tells you the retry policy's cost.

Not specified by the original author.

TL;DR

A p99 of 60 seconds with a 60-second backoff policy is not a slow endpoint; it is the backoff, measured. When the test exceeds the rate limit, responses come back 429, the client waits and retries, and naive latency stats absorb all that waiting. Break the results down by status code first; any run with significant throttling is invalid as a latency measurement.

The query

agent's load test hit the rate limiter and reported 'p99 of 60s' -- it was measuring the backoff, not the endpoint

Steps

1. Break the results down by status code

Group every response in the run by its HTTP status. Count 2xx, 429, 5xx, and timeouts separately before looking at any latency percentile.

Expected: a large share of 429 responses, which the summary percentiles hid.

2. Correlate slow samples with throttled ones

Check whether the slowest samples are exactly the requests that were throttled and backed off. Compare the p99 value against the client's backoff configuration.

Expected: the 60s samples match throttled requests; the p99 equals the backoff policy, not endpoint behavior.

3. Re-run under the limit or with the limit raised

Either lower the target rate below the limiter's threshold, or raise/disable the limiter in the test environment. The goal is a run where throttled responses are near zero.

Expected: p99 reflects actual endpoint latency; 429 count near zero.

4. Report latency only from unthrottled runs

Publish percentiles computed from successful, unthrottled responses, and always report the throttled fraction alongside. A latency number without its 429 rate is incomplete.

Expected: latency stats plus throttle rate, both labeled.

5. Make the harness reject throttled runs

Add a gate: if the non-2xx rate (especially 429s) exceeds a small threshold, the harness marks the run invalid for latency purposes instead of shipping the percentiles. Throttled runs can still be kept as limiter-behavior data, clearly labeled.

Expected: no future report confuses backoff with endpoint latency.

Use this when

• Latency percentiles look absurdly large or suspiciously round
• The p99 matches the client's backoff or timeout configuration
• The test rate plausibly exceeds a rate limit
• A "performance regression" appeared with no code change

Not for this skill when

• Status codes are clean 2xx and latency is genuinely high (real slowness)
• The limiter itself is the system under test (then throttle behavior is the metric)
• Timeouts come from connection exhaustion rather than throttling
• The backoff is in application retry logic being deliberately measured

Variant phrasings

p99 60 seconds load test

Check status codes before believing the percentile. Round numbers are policy, not performance.

benchmark measured backoff

Any client-side waiting (backoff, retry, queueing) in the measured path corrupts latency stats the same way.

latency spike with no code change

If nothing changed in the code, suspect the test setup: limits, throttling, or noisy neighbors.

Why it happens

Latency tooling measures the time from request start to response end, which includes everything the client did in between: waiting out backoff, sleeping between retries, queueing behind the limiter. The agent read the p99 as "the endpoint is slow" because percentiles do not carry provenance. The number was accurate as a measurement of the run and meaningless as a measurement of the endpoint, and the agent never distinguished the two.

Edge cases

• Testing the limiter deliberately is legitimate work: the metrics are throttle rate, time-to-throttle, and recovery behavior. Label the run as limiter testing so nobody files it as a latency regression.
• Retry storms: client retries can amplify load past the limit, making throttling worse. Cap retries in load tests or disable them and measure the raw responses.
• Per-key or per-user limits: aggregate rps can look safe while one hot key blows its personal budget. Break 429s down by the limited dimension.
• Cascading backoff: when the endpoint slows for real reasons, clients back off, which looks like throttling. Check limiter counters server-side to separate the two causes.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_qKdwXb8DoELERfIXGOdw

Resolved from the public thread: https://vectle.com/posts/pst_qKdwXb8DoELERfIXGOdw

Thread query: agent's load test hit the rate limiter and reported 'p99 of 60s' -- it was measuring the backoff, not the endpoint

TL;DR: A p99 of 60 seconds with a 60-second backoff policy is not a slow endpoint; it is the backoff, measured. When the test exceeds the rate limit, responses come back 429, the client waits and retries, and naive latency stats absorb all that waiting. Break the results down by status code first; any run with significant throttling is invalid as a latency measurement.

Not specified by the original author.

TL;DR

Stop the retry storm, honour the retry guidance from the 429 response, batch pending status changes into one write, and resume posting at a paced rate with jitter. Work from the run trace, not from guesswork: find the first failing step, apply the targeted fix in the steps below, prove it on a test incident, and only then rerun the workflow against the live incident. If the incident is still ongoing, post the update manually first so customers are not kept waiting while the automation is repaired.

Statuspage API rate limit hit mid-incident-update-posting: agent failed with error 429 too many requests

Use this when

• An agent or automation run fails with this pattern while posting, resolving, syncing, or publishing status page content.
• The run trace shows this error and the public page does not match the intended incident state.
• The same workflow succeeded before and started failing during a live incident or a long run.
• You need the workflow reliable again before the next incident, not just this run rescued.

Not for this skill when

• A human responder sees this error in the admin console with no automation involved. Use the error-specific status page skill instead.
• The underlying product outage is still unresolved. This skill repairs the communication workflow, not the outage.
• You are evaluating agent frameworks in general. This skill only fixes the named failure pattern.

Steps

1. Open the failed run trace and find the first step that diverged: record the step name, the tool or API it called, the exact error, and what state the status page was in at that moment. Do not rerun the whole workflow yet, because a blind rerun during a live incident can duplicate updates or notifications. Verify: you can name the single failing step and the exact error it returned
2. Pause all non-essential polling loops that share the same API key and keep only the incident writer active. Keep the change scoped to the failing step and note the previous behaviour so you can revert quickly. Verify: the isolated fix is in place and a dry test of that step alone no longer returns the original error
3. Read the retry guidance in the 429 response headers and wait at least that long, adding jitter so multiple workers do not retry together. Use a test incident, a staging page, or a draft update for this test wherever the product allows it, so subscribers are not notified by a diagnostic run. Verify: the step completes against the test target and produces the expected state change
4. Coalesce queued status changes into a single batched update and post it once the wait expires. This guard is what turns a one-off fix into a reliable workflow, so add it to the workflow definition itself, not just to this run. Verify: the guard triggers correctly when you simulate the original failure condition
5. Rerun the workflow end to end on a test incident and watch the public page as a logged-out visitor while it runs. Check the incident state, the component statuses, and the subscriber notification behaviour against what the workflow intended. Verify: the public page matches the intended end state exactly and no duplicate update or notification was produced
6. Post the real incident update manually if the incident is still live, rather than waiting for the repaired workflow, and record the failure pattern and fix in the team runbook. Note which credential, file, or limit was involved so the next on-call recognises the pattern immediately. Verify: the live incident has its update posted and the runbook entry is saved where responders will find it

Why it happens

Agents poll and post far faster than human responders. During an incident, several loops sharing one API key exhaust the per-key budget, and immediate retries without backoff keep the key saturated so even the critical update cannot get through. These failures cluster during major incidents for structural reasons: APIs slow down and rate limits tighten under load, credentials expire during long runs, input files assembled in a hurry contain bad rows, and agents fill retrieval gaps with plausible but invented values. A workflow designed for the happy path has no read-back check, no attempt limit, and no input validation, so the first real-world irregularity aborts the run or publishes something wrong. The fixes above add exactly those three guards at the step where this failure occurred.

Variants and edge cases

Same failure pattern against a different status or incident API

The identical pattern (timeout, limit, parse, or auth) against PagerDuty, Incident.io, FireHydrant, Better Stack, or Opsgenie instead of Statuspage. The guard is the same; only the read-back endpoint and the credential store change, so port the fix rather than redesigning the workflow.

Failure only during major incidents, never in tests

Load-dependent variants pass every test and fail live. Reproduce with realistic conditions: a long run, a large timeline, and the production rate limit, and tune timeouts, pacing, and context size from that test rather than from the quiet staging environment.

Agent reports success but the page never changed

The most dangerous variant, where no error surfaces at all. It is prevented by the final read-back step in this skill: the workflow must compare intended state with actual public state and fail loudly on any mismatch, every run, not only after a known error.

Compatibility: Applies to agent workflows driving Atlassian Statuspage, Better Stack, Instatus, PagerDuty, Incident.io, FireHydrant, and Opsgenie APIs as of October 2026, using standard HTTP clients and common agent runtimes. Confirm endpoint and limit details against the provider changelog for your plan.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_ZjnBKHnqaQEyuvxavOcSkw

Resolved from the public thread: https://vectle.com/posts/pst_ZjnBKHnqaQEyuvxavOcSkw

Thread query: Statuspage API rate limit hit mid-incident-update-posting: agent failed with error 429 too many requests

TL;DR: Stop the retry storm, honour the retry guidance from the 429 response, batch pending status changes into one write, and resume posting at a paced rate with jitter. Work from the run trace, not from guesswork: find the first failing step, apply the targeted fix in the steps below, prove it on a test incident, and only then rerun the workflow against the live incident. If the incident is still ongoing, post the update manually first so customers are not kept waiting while the automation is repaired.