Automate Cucumber with AgenticOps

The operational work this removes

• p99 latency creeps up release over release and nobody notices until a customer complains or an SLO burns
• over-provisioned instances and idle capacity quietly inflate the cloud bill with no owner watching utilization
• performance investigations stall because the on-call engineer has to manually correlate traces, profiles, and metrics across three tools at 2am
• load and profiling runs happen once before launch, then drift as traffic patterns and code paths change
• connection-pool exhaustion, N+1 queries, and memory leaks recur because the root-cause fix never gets prioritized after the fire is out
• capacity planning is a quarterly spreadsheet guess instead of a data-driven forecast tied to real growth

From signal to verified action

CloudThinker's Frontier Investigation Agents investigate the signal, find the why and propose the fix, or run the safe action your policy allows. Your engineers approve, and the agents verify the outcome.

1. Detect

   Detect the Cucumber signal

   Agents continuously watch Cucumber signals — latency percentiles (p50/p95/p99), CPU and memory saturation, throughput, queue depth, connection-pool usage, and cost-per-request — against SLO baselines and historical trend. A statistically significant regression, a saturation threshold, or a right-sizing opportunity opens a finding automatically instead of waiting for a human to notice.

2. Analyze

   Analyze the root cause

   For each finding the agent correlates traces, flame graphs/profiles, deploy markers, and infra metrics to isolate the actual bottleneck — a hot code path, an N+1 query, a starved connection pool, an under-provisioned node, or a cache miss cliff — rather than the symptom. It writes a plain-language root-cause narrative with the evidence attached, and quantifies the expected latency or cost impact of fixing it.

3. Remediate

   Remediate under policy

   The agent proposes a concrete fix-plan graded by autonomy level: L1 recommend-only, L2 open a PR / draft the config change, L3 auto-apply low-risk reversible changes (HPA target, pool size, cache TTL, instance right-sizing) inside a sandbox with a canary, L4 fully autonomous for pre-approved change classes. Every action uses brokered credentials and stays within team policy — engineers stay on the loop with an approval gate on anything above their configured threshold.

4. Verify

   Verify and record

   After applying, the agent re-runs the relevant load/profiling check and compares post-change latency, saturation, and cost against the pre-change baseline. If the target metric improved and no SLO regressed, it closes the finding with a tamper-evident audit record; if the change underperforms or degrades another signal, it auto-rolls back and re-opens analysis with the new data.

Evidence and proposed action

$ cloudthinker perf plan --skill "Cucumber" --service checkout-api

DETECT   p99 latency 412ms → 918ms since deploy a3f91c (+123%), SLO 500ms breaching
ANALYZE  root cause: connection pool saturated (max=20, in-use=20, wait_avg=340ms)
         evidence: trace 7c2e..→ db.acquire dominates; +2.1 QPS/pod vs baseline
         blast radius: checkout-api (3 pods) · no schema/data change required

FIX-PLAN (autonomy L3 · reversible · canary 10%)
  1. raise HikariCP maximumPoolSize 20 → 40   (IaC: infra/checkout/pool.tf)
  2. set pool acquire-timeout 30s → 5s (fail fast)
  3. right-size: t3.large → t3.xlarge only if p99 still > SLO after step 1

VERIFY   canary 10% for 15m → p99 918ms → 447ms (-51%), pool wait 340ms → 12ms
         cost delta +$0/mo (no scale-up needed) · no SLO regressions
         ✔ promote to 100%   ↻ auto-rollback armed   🔒 audit: evt_9d4b2

Approve? [L3 auto-apply within policy — engineer on the loop]

What the agent understands

Cucumber BDD testing management and analysis. Covers feature file organization, step definition mapping, scenario tagging, Gherkin syntax validation, test report parsing, and step reuse analysis. Use

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