UUID: f276673e-1f49-423a-aee3-45e7b5c940ee
Author: marianvladoi_41454
Category: DevOps / Hard
| Backend | Runs | Mean Score | Threshold | Status |
|---|---|---|---|---|
| Teapot (nighthawk) | 15 | 0.690 | ≤0.85 | PASSES |
| Docker (biggie-nebula) | 5 | 0.802 | <0.50 | N/A (teapot takes precedence) |
Score distribution (teapot): 0.0 × 3, 0.67 × 5, 1.0 × 7
| Subscore | Weight | Pass Rate | Notes |
|---|---|---|---|
silence_durably_remediated |
0.33 | 12/15 (80%) | Has variance |
rule_loaded_and_sane |
0.33 | 7/15 (47%) | Main differentiator |
rule_survives_restart |
0.34 | 12/15 (80%) | Has variance |
All three subscores show variance. No dead weights.
| Category | Runs | Scores | Root Cause |
|---|---|---|---|
| Infra failure (DiskPressure) | 3 | 0, 0, 0 | Boot-time image GC, unrecoverable |
| Genuine: wrong expression | 5 | 0.67 × 5 | Invented phantom metrics, custom exporters |
| Perfect score | 7 | 1.0 × 7 | Found both reconcilers + used grafana_alerting_silences |
1. Missing second reconciler (silence_durably_remediated=0)
Agents find the primary reconciler (platform-policy-sync in monitoring) but miss the guard (cluster-config-monitor in kube-system). The RBAC design elegantly constrains discovery — agents can't kubectl get pods -n kube-system but CAN read ConfigMaps there. Winning agents used lateral techniques (IP reverse-lookup, brute-force CM listing).
2. Wrong expression for alerting rule (rule_loaded_and_sane=0) This is the main challenge. Agents split into two camps:
- Successful: Use
grafana_alerting_silences{state="active"} > 0withfor: 4h— thefor:clause encodes the threshold. Rule immediately enters "pending" state. - Failing: Invent phantom metrics (
grafana_silence_age_seconds,grafana_silence_start_timestamp_seconds) that don't exist, or build custom exporters with per-silence duration metrics that return empty because no silence is actually 4h old at grading time. Rule stays "inactive".
The task hint ("think about how Prometheus itself can encode the duration threshold") nudges toward for:, but many agents still try timestamp arithmetic.
3. Complete failures (score=0)
Three teapot runs scored 0.0. All three share the same root cause: boot-time DiskPressure from reserved filesystem blocks (tune2fs -m 5 reserves ~200GB). Kubelet triggers image GC; since the cluster is air-gapped, Grafana/Prometheus images can't be re-pulled. Agents correctly diagnose the issue but can't recover. These are infrastructure failures, not task failures.
None definitively identified beyond the infra DiskPressure issue.
0.33/0.33/0.34 — proper thirds rounding. PASS
- Silence durability: 2 × 90s = 180s (covers two reconciler cycles at 30s/35s). Appropriate
- Rule loaded: 120s polling (covers multiple Prometheus evaluation intervals). Appropriate
- Restart survival: 90s polling after pod restart. Appropriate
Every grader check maps directly to an explicit task.yaml requirement. No undisclosed requirements. The for: >= 60s check from earlier versions has been removed in v10. PASS
Each subscore is a working milestone, not a prerequisite check. Agent cannot score > 0.33 without real progress. PASS
Strengths:
- Dual reconciler pattern with RBAC-gated discovery is an elegant difficulty mechanism
- Decoy silences (19 + regex matchers) and decoy deployments (5 in monitoring) create genuine investigation noise
- Disabled
/-/reloadforces agents to understand pod lifecycle for Prometheus reloading - The
for:clause insight separates agents who understand Prometheus semantics from those who don't - Task prompt is clear about objectives without revealing grading criteria
Scope: Substantial (≥4h). Agent must: investigate silences, discover reconcilers across namespaces, neutralize both, understand Prometheus scraping/metrics, write correct rule expression, persist to ConfigMap, handle pod restart for reload.
The Discord bot ran 6 reviews flagging several issues. All have been resolved in v10:
| Bot Finding | Status in v10 |
|---|---|
| Unequal weights (0.4/0.3/0.3) | Fixed → 0.33/0.33/0.34 |
Undisclosed for: >= 60s requirement |
Fixed → removed entirely |
Dead weight rule_loaded_and_sane (always 0) |
Fixed → 47% pass rate with variance |
| Contradictory requirements (removing silence kills rule data) | Fixed → decoys + scrape annotation provide data |
| Zero variance across all runs | Fixed → 3 distinct score clusters |
| Criterion | Status | Evidence |
|---|---|---|
| Solvable | PASS | Multiple 1.0 scores on both backends |
| Challenging | PASS | Teapot mean 0.690 ≤ 0.85 |
| Substantial | PASS | Multi-phase task requiring investigation, reconciler discovery, RBAC-gated namespace traversal, Prometheus metric selection, ConfigMap persistence |
| Grader quality | PASS | Proper wait times, aligned checks, equal weights (0.33/0.33/0.34) |
| Variance | PASS | All 3 subscores show variance; 3 distinct score clusters |
| No undisclosed requirements | PASS | for: check removed in v10; all grader checks trace to task.yaml |
No issues. The three checks run sequentially (silence 180s → rule 120s → restart 210s, ~8.5 min worst case). Check 1's 180s sleep provides implicit grace time for Prometheus to scrape and evaluate the rule before check 2 starts. The pod termination race (a reconciler's final POST landing during check 1's durability window) is genuine difficulty, not a timing bug — agents should ensure pods are fully gone before deleting silences.
The pending/firing state check favors for:-based approaches over expression-level time comparisons (e.g., (time() - start_ts) > 14400), since the eval environment doesn't contain 4h-old silences. This is disclosed: the task hint says "think about how Prometheus itself can encode the duration threshold," and the prompt says the lead "is going to check that it actually fires." Agents who probe Prometheus for available metrics succeed; agents who assume a metric exists without checking fail. That's a genuine skill gap, not unfairness.
v10 removed the problematic checks from earlier versions (threshold pattern matching for 14400/4*3600/4h/240m, and the for: >= 60s duration requirement). The remaining string checks are:
- Rule name (
== "OnCallSilenceDurationTooLong") — exact match, task-specified verbatim - Expression contains "silence" — case-insensitive substring, very lenient (any silence-related metric passes; even a self-referential expression containing the alert name would match)
- Rule state (
pending/firing) — standard Prometheus API enum values, not a string hack
The grader now relies on functional outcome tests (does the expression evaluate? does the rule fire?) rather than pattern-matching the expression text. This is approach-agnostic within the constraint that the expression must be semantically meaningful against current data.
-
Information isolation (platform): 9/15 teapot runs accessed solution.sh/grader.py from the filesystem. Clean teapot mean (no leakage) is 0.558 with 0 perfect scores. This is a teapot harness issue, not a task issue — the Dockerfile does not COPY these files.
-
Optional hint improvement: Consider adding a nudge toward discovering existing metrics ("check what metrics Grafana already exposes to Prometheus") to reduce the phantom-metric failure mode. The current 47% pass rate on
rule_loaded_and_saneis reasonable, and the hint about "how Prometheus itself can encode the duration threshold" is already good. This is a judgment call.