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AgenticOps automation · Performance

Automate API Latency Investigation with AgenticOps

Turn API Latency Investigation from a manual, one-off investigation into a continuous AgenticOps loop. CloudThinker runs API Latency Investigation through autonomous AI agents — under team policy, with brokered credentials, sandboxed execution, deterministic data tokenization, and tamper-evident audit — so latency regressions, resource waste, and capacity risks get caught and fixed before they page anyone.

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Grounded in your stack
Controlled by your policy
Verified after every action

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 investigates the signal, proposes or executes the safe action your policy allows, then verifies the outcome.

01 · Detect

Detect the API Latency Investigation signal

Agents continuously watch API Latency Investigation 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.
02 · 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.
03 · 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.
04 · 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 "API Latency Investigation" --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

Provides a systematic investigation framework for diagnosing and resolving API latency issues. Covers distributed tracing analysis, bottleneck identification across the request path, database query im

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