Task Review: Jaeger Query Service Index Mapping Conflict

Task ID: jaeger-query-service-indexmapping-conflict UUID: 378d049f-e957-4c78-9fa8-d1b84684d37e Version: 12 Author: chrorrala (Christian Orrala) Category: Platform Engineering Reviewer: Dylan Fitzgerald Date: 2026-04-03

Verdict: APPROVE

Acceptance Criteria

Criterion	Result	Evidence
Solvable	PASS	`test-solution` scores 1.0 (all 4 subscores pass)
Challenging	PASS	Teapot mean 0.344 ≤ 0.85 threshold
Substantial	PASS	Multi-domain: Jaeger/Badger, GitOps (Gitea+ArgoCD), OTEL, Mattermost investigation; ≥4 hours

Eval Summary

Scores by Backend (v12 only)

Backend	Runs	Scores	Mean	Threshold	Status
Docker (Batch 2)	5 v12 runs	[1.0, 1.0, 0.25, 1.0, 1.0]	0.85	<0.50	Above threshold
Teapot (Batch 3)	8	[0.25×7, 1.0×1]	0.344	≤0.85	Under threshold

Acceptance path: Teapot. Docker scores are above the <0.50 Docker threshold but irrelevant — teapot results are well within acceptance range.

Note: Batch 1 (7 runs) was v11 and excluded from this analysis. Batch 2 run 4 was also v11 (stale local file from prior download) and excluded, leaving 5 v12 Docker runs.

Per-Check Breakdown (Teapot, v12)

Check	Pass Rate	Pattern
`index_cleanup`	1/8 (12.5%)	Blocked by rename direction choice
`e2e_trace`	1/8 (12.5%)	Blocked by rename direction choice
`config_standardized`	1/8 (12.5%)	Blocked by rename direction choice
`storage_hardening`	8/8 (100%)	All agents discover and configure TTL

The task has a clear difficulty gate: agents who identify bleat-api as the correct service name pass everything; agents who choose bleat-service max out at 0.25 (storage_hardening only).

Infrastructure Health

All 8 teapot runs are infrastructure-successful:

storage_hardening = 1.0 across all runs (proves Jaeger, kubectl, grading pipeline functional)
No grader errors (no "Could not query Jaeger", no timeouts)
All runs have 200+ messages (agents had full working sessions)
1 perfect run confirms end-to-end solvability on teapot

Note on transcript completeness: the download script retrieved 7 of 8 teapot transcripts. The 8th was lost to a naming collision (two rollouts both labeled b3.1 by the API; second overwrote first). Both scored 0.25, both are v12, and the surviving one shows the same subscore pattern as all other 0.25 runs. No gap in coverage.

Failure Analysis

Classification: All Failures Are Genuine

Root cause: Agents discover the Mattermost breadcrumb stating the team renamed bleat-service to bleat-api, but override it because environmental pattern signals are stronger:

All other Bleater services use the -service suffix
The Kubernetes deployment is named bleater-bleat-service
The source code has init_tracing("bleat-service")
A sed command in the deployment rewrites it to bleat-api at startup — agents interpret this as the "hack" that needs reverting

Evidence from transcripts:

Every failing agent found and read the Mattermost message
Multiple agents explicitly acknowledged bleat-api as the team's stated intent, then overrode it
b3.4 shows extended "Option A vs Option B" deliberation, ultimately choosing pattern-matching
b3.7 explicitly writes: "The -service convention is the standard. The sed that renames to bleat-api is WRONG — it's the drift."
b3.2 states: "The deployment is producing traces as bleat-api but it should be bleat-service."
The sole passing teapot run (b3.5) immediately accepted the Mattermost signal: "The team is migrating TO bleat-api — the OTEL_SERVICE_NAME env var still says bleat-service — THIS is the drift!"

Why this is genuine difficulty, not a coin flip: The agents aren't guessing randomly. They're making a systematic diagnostic reasoning error: prioritizing structural pattern-matching over explicit human institutional knowledge. In real incident response, the team's stated intent in comms channels IS the source of truth for what a rename was supposed to achieve. Agents that override that signal with "but the naming convention says otherwise" are making a real diagnostic mistake. The author deliberately designed this as a skill gate, and it's working as intended.

No artificial failure indicators:

No infrastructure issues (storage_hardening passes universally)
No grader bugs (passing runs confirm all checks work)
No timing issues (60-minute freshness window is generous)
Multiple distinct failure modes not required — the single failure mode is a genuine reasoning error, not a blocked path

Task Quality Assessment

Grader Quality

Scoring structure: 4 subscores, equally weighted (0.25 each), each binary — compliant with platform requirements.

Subscore	Sub-checks	What it validates
`index_cleanup`	min(service_name_clean, no_bleat_service_traces, operation_list_clean)	Badger cleaned, bleat-api active, operations correct
`e2e_trace`	Single check	Real api-gateway → bleat-api distributed trace exists
`config_standardized`	min(service_audit, otel_config)	OTEL_SERVICE_NAME=bleat-api + all services audited
`storage_hardening`	Single check	Badger TTL ≤168h configured

Strengths:

TTL check supports 3 configuration paths: container args, env vars, AND ConfigMaps (Jaeger v2 style) — no false negatives from valid alternative approaches
OTEL config check supports both direct env vars and ConfigMap — same principle
60s stabilization wait before grading, 60-minute trace freshness window — generous
Anti-gaming analysis documented in grader docstrings for every check
legacy_traces_cleaned is correctly run as diagnostic-only (not scored)
Traffic generator deployment ensures live traces flow continuously

No issues found with wait times, grader alignment, or gameable checks. Each subscore represents a working milestone (not file existence or syntax checks). Agent cannot score >0.25 without meaningful progress on the core problem.

task.yaml Quality

Clear incident report with specific symptoms (HTTP 500s, mixed service names, broken parent-child spans)
Sufficient environment context (namespace, URLs, constraints)
Does not reveal grading criteria — agent must investigate to determine the fix
Does not overspecify solution approach — multiple valid paths exist
CI/CD constraint ("All persistent changes must be done through the CI/CD pipeline") adds realistic difficulty via ArgoCD selfHeal
Minor: "the on-call team is blocked they cannot" (line 32) has missing punctuation — cosmetic only

Information Isolation

Dockerfile: Clean — only sets base image and ALLOWED_NAMESPACES. No COPY of solution.sh or grader.py
setup.sh: Creates sabotage via GitOps and Mattermost breadcrumbs. Does not reveal the fix
task.yaml: Describes symptoms, not grader checks
ALLOWED_NAMESPACES: bleater,observability,argocd — appropriate for this task's scope
No prior-run artifacts leak between evaluations

Scope

The task spans multiple domains:

Jaeger/Badger: Understanding index collision, cleaning stale data, configuring retention
GitOps: Discovering ArgoCD selfHeal prevents direct kubectl fixes; committing through Gitea
OpenTelemetry: Diagnosing OTEL_SERVICE_NAME misconfiguration, understanding init_tracing vs env var precedence
Mattermost investigation: Finding and correctly interpreting the institutional knowledge breadcrumb
Service audit: Verifying no other Bleater services have naming drift

This is cohesive (all related to one incident), has clear start/end states, and easily exceeds the 4-hour threshold.

Observations (Non-Blocking)

storage_hardening is effectively free points (100% pass rate on teapot). Every agent discovers and configures Badger TTL. This sets a 0.25 floor on all scores and compresses the scoring range. If difficulty needs tightening in the future, consider grouping storage_hardening with another check via subscore consolidation (not reweighting).
Docker vs teapot pass rate divergence is notable. Docker: 4/5 agents (80%) chose the correct rename direction. Teapot: 1/8 (12.5%). Same model, same v12 task. This could be stochastic variance (small samples), or a subtle difference in how the system prompt frames the Mattermost context. Worth monitoring if additional evals are run, but not blocking.
The legacy_traces_cleaned diagnostic check always passes (satisfied by any Badger wipe) and adds noise to the feedback output. Consider either removing it from feedback or documenting it more prominently as unscored.
TTL threshold (≤168h) is undisclosed in task.yaml (which says "hardened against future accumulation of stale data"). This is fine in practice — all agents set reasonable values and pass — but the reviewer bot flagged it in earlier versions. The current 168h ceiling is generous enough that no agent has been unfairly penalized.

Iteration History

This task has been through 12 versions with extensive feedback cycles:

v1–v5: Rename direction ambiguity was too strong; Mattermost breadcrumb was broken or insufficient. 0% pass rate.
v5–v8: Mattermost breadcrumb fixed and strengthened. Grader expanded to support ConfigMap-based TTL. Dead-weight subscores consolidated.
v9–v11: Subscores consolidated from 8 to 4 groups. CI/CD constraint added (GitOps requirement). TTL threshold relaxed from ≤72h to ≤168h.
v12 (current): Final polish. All nebula-reviewer checks pass (65/65). Teapot eval confirms difficulty threshold met.

The author has been responsive to feedback throughout, addressing each issue systematically while preserving the task's core design intent.

arubis/jaeger-index-mapping-review.md

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