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Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) on Root-cause a checkout 500 spike from nginx/app/deploy logs with red herrings

AClaude Haiku 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-16 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first try
Estimated cost
$0.0083
Wall time
21s
Human step-ins
0
Outcome
Pass

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Summary

Exact prompt

# Task: incident root cause from logs On 2026-10-12 users reported failed checkouts. `logs/` contains `access.log` (nginx), `app.log` (application) and `deploys.log`. All times UTC. Write `incident.json`: {"incident_start_minute": "HH:MM", "incident_end_minute": "HH:MM" (last minute with app 500s), "root_cause": one of ["db_pool_exhaustion","cdn_outage","slow_search_query","bad_ranking_deploy","traffic_spike","other"], "culprit_commit": "<sha>", "fix_commit": "<sha>", "most_affected_endpoint": "<path>", "app_500_count": <int, HTTP 500 responses with upstream=app>} Then write `summary.md` (≤150 words) for the status page. Note there may be unrelated noise in the logs. No network.

Find the root cause of a spike in failed checkouts from nginx, application and deploy logs. The agent must tie a deploy's config change to database connection-pool timeouts, give the exact incident window and count of 500 errors from the app, and ignore red herrings such as a CDN blip and slow cron warnings. It also writes a short incident summary. Original benchmark task written for usecaserank.com (not based on a specific public post). Headless, offline run on a reproducible fixture. Each run's notes say how its cost was measured (estimates are marked).

Success criteria: 1. root_cause enum must be exactly right. 2. culprit_commit and fix_commit must match (prefix match on the short SHA). 3. most_affected_endpoint must be exact. 4. incident_start_minute within ±1 minute and incident_end_minute within ±2 minutes of the truth. 5. app_500_count must be the exact count of HTTP 500 responses with upstream=app. 6. summary.md must exist, be 1–150 words, and must not attribute the cause to the CDN or the search query.

Setup notes: Use Case Scout batch 3 case c08-log-forensics (offline fixture). Prepared with batch3/prepare.sh; hidden key/tests kept outside the agent's work dir. Prompt = TASK.md verbatim. Scored with the scout's scorer: `python score.py WORKDIR KEYDIR/key.json`. Tools: Read/Write/Edit/Glob/Grep/Bash, web disabled. Single run, no retries. Claude Code's own reported cost for claude-haiku-5.5: $0.008287. Score: 8/8 checks. Cost is an ESTIMATE: measured tokens x pricing.json list price (Claude Pro subscription, nothing billed); input_tokens include cache reads and writes. Single run.

Estimated cost List-price estimate; not actually billed.

Metrics

Estimated cost
$0.0083
Wall time
21s
Human step-ins
0
Turns
8
Model usage
ModelInput tokOutput tokCost
AClaude Haiku 5.5anthropic/claude-haiku-5.5effort default (no --effort flag) · thinking on204,8093,899$0.0083

Reproduce this run

  1. Use the use case prompt on the use case page.
  2. Same setup: Claude Haiku 5.5 (anthropic/claude-haiku-5.5, effort default (no --effort flag) · thinking on) via Claude Code 2.1.299 (headless -p), cloud.
  3. It counts as a success only if it meets every success criterion: 1. root_cause enum must be exactly right. 2. culprit_commit and fix_commit must match (prefix match on the short SHA). 3. most_affected_endpoint must be exact. 4. incident_start_minute within ±1 minute and incident_end_minute within ±2 minutes of the truth. 5. app_500_count must be the exact count of HTTP 500 responses with upstream=app. 6. summary.md must exist, be 1–150 words, and must not attribute the cause to the CDN or the search query.
  4. Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.

How runs are scored: Methodology.

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Automated checks

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Evidence log
[
 {
  "duration_api_ms": 19893,
  "stop_reason": "end_turn",
  "session_id": "[REDACTED]",
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  "usage": {
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   "cache_read_input_tokens": 182224,
   "output_tokens": 3899,
   "output_tokens_details": {
    "thinking_tokens": 1141
   },
   "server_tool_use": {
    "web_search_requests": 0,
    "web_fetch_requests": 0
   },
   "service_tier": "standard",
   "cache_creation": {
    "ephemeral_1h_input_tokens": 22571,
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   "inference_geo": "global",
   "iterations": [
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     "input_tokens": 2,
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     "cache_creation": {
      "ephemeral_5m_input_tokens": 0,
      "ephemeral_1h_input_tokens": 427
     },
     "type": "message"
    }
   ],
   "speed": "standard",
   "fallback_credit": null
  },
  "modelUsage": {
   "claude-haiku-5-5": {
    "inputTokens": 14,
    "outputTokens": 3899,
    "cacheReadInputTokens": 182224,
    "cacheCreationInputTokens": 22571,
    "webSearchRequests": 0,
    "costUSD": 0.00828734,
    "contextWindow": 1000000,
    "maxOutputTokens": 128000,
    "thinkingTokens": 1141,
    "canonicalModel": "claude-haiku-5-5",
    "provider": "firstParty",
    "costBasis": "list"
   }
  },
  "permission_denials": [],
  "terminal_reason": "completed",
  "fast_mode_state": "off",
  "fast_mode_disabled_reason": "sdk_opt_in_required",
  "subagent_stats": {
   "spawned": 0,
   "requested": {
    "background": 0,
    "foreground": 0,
    "unset": 0
   },
   "started_in_background": 0,
   "max_depth": 0,
   "spawned_by_subagents": 0,
   "completed": 0,
   "failed": 0,
   "killed": {
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   "refused": {
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   "by_type": {}
  },
  "safety_stops": 0,
  "is_error": false,
  "num_turns": 8,
  "subtype": "success",
  "api_error_status": null,
  "result": "I wrote `incident.json` and `summary.md` in the work directory. The root cause is database connection pool exhaustion, triggered by the 14:05 UTC deploy `4be7d19`.\n\n**Timeline**\n- **12:40:** `9f31c2a` deploys a search ranking change with no config changes. Nothing in the logs points to it.\n- **13:30\u201313:35:** `cron.reindex` logs slow `products_search` queries. The app marks them non-blocking and no errors follow, so I treated this as noise.\n- **14:05:** `4be7d19` cuts `DB_POOL_SIZE` from 20 to 5 and `DB_POOL_TIMEOUT_MS` from 10000 to 5000.\n- **14:07:** the first pool timeouts and HTTP 500s appear, and waiting requests build up to 40 at pool size 5.\n- **14:29:47:** oncall reverts with `c02aa51`, restoring pool size 20 and the 10s timeout.\n- **14:30:** the last minute with app 500s.\n\n**Values in `incident.json`**\n- `incident_start_minute`: `14:07`\n- `incident_end_minute`: `14:30`\n- `root_cause`: `db_pool_exhaustion`\n- `culprit_commit`: `4be7d19`\n- `fix_commit`: `c02aa51`\n- `most_affected_endpoint`: `/api/checkout` (163 of 319 failures; `/api/cart` had 156)\n- `app_500_count`: `319`\n\n**Choices to check**\n- The logs only give short SHAs, so I used those.\n- I counted 500s by the exact status field. A looser match picks up byte counts of 500 and inflates the total to 321.\n- \"Most affected\" means most 500 responses, not error rate. Checkout leads cart by only 7, so a rate-based answer could differ.\n- The 14:39 CDN purge and the 500 served by `cdn-edge` happened outside the incident window, so I ruled them out.\n\n`summary.md` is 134 words, written for the status page, and says the issue is resolved.",
  "ttft_ms": 828,
  "type": "result",
  "duration_ms": 20384,
  "uuid": "[REDACTED]",
  "ttft_stream_ms": 778,
  "time_to_request_ms": 233,
  "first_content_frame_ms": 778,
  "queued_turn_count": 0,
  "result_index": 0
 }
]

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