AgenticOps automation · Cost

Automate cloud cost reduction with GCP Rightsizing on AgenticOps

GCP Rightsizing turns cloud cost analysis into safe, closed-loop action. AgenticOps is the discipline of running production cloud operations through autonomous AI agents — under team policy, with brokered credentials, sandboxed execution, deterministic data tokenization, and tamper-evident audit — so a rightsizing or waste-elimination finding from GCP Rightsizing becomes a reviewed, reversible change instead of a spreadsheet nobody actions.

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Grounded in your stack

Controlled by your policy

Verified after every action

The operational work this removes

Rightsizing and idle-resource reports pile up but nobody has time to safely action them across hundreds of accounts
Savings Plans and Reserved Instance coverage drifts, leaving on-demand spend on the table month after month
Idle EBS volumes, unattached EIPs, orphaned snapshots, and stopped-but-billed resources quietly accrue waste
Cost anomalies are caught days late in the monthly bill instead of the hour they start
Engineers fear that automated cleanup will delete something still in use, so nothing gets automated
Kubernetes and shared-cluster spend is invisible per team, blocking chargeback and accountability

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 GCP Rightsizing signal

GCP Rightsizing continuously watches Cost Explorer, CUR/billing exports, and utilization metrics to surface concrete savings signals — underutilized EC2/RDS instances, idle and unattached resources, expiring or under-covered commitments, and spend anomalies — each tied to a specific resource ARN and a dollar/month estimate rather than a vague trend.

02 · Analyze

Analyze the root cause

Each finding is enriched with context before any action: real utilization percentiles over a lookback window, tags and owner, environment (prod vs non-prod), workload criticality, and dependency/attachment checks. The agent computes the expected monthly savings, the rollback path, and a confidence score, and drops low-signal or in-use resources so only credible, reversible changes advance.

03 · Remediate

Remediate under policy

Approved changes are executed through brokered, scoped credentials in a sandboxed session — resize an instance, purchase or rebalance a Savings Plan to a target coverage, delete a confirmed-orphaned volume after snapshot, or schedule non-prod shutdown. Graduated autonomy (L1 Notify, L2 Suggest, L3 Approve, L4 Auto) lets teams keep engineers on the loop: a snapshot-then-delete of an untagged idle disk may run at L4, while a prod rightsize or a multi-year commitment stays gated at L3 approval.

04 · Verify

Verify and record

After every change the agent confirms the outcome — the resource is healthy at its new size, the commitment coverage hit target, the deleted resource had a restorable snapshot — and re-measures realized spend against the estimate. Every step (who connected, what was reached, what changed, who approved) lands in a tamper-evident audit trail, and any regression triggers an automatic rollback or re-open of the finding.

Evidence and proposed action

$ agenticops plan --skill "GCP Rightsizing" --scope prod-account-4021

FIX PLAN  (3 findings · est. savings $4,180/mo)  autonomy: on-the-loop

[1] Rightsize  i-0a9f...c31 (m5.4xlarge → m5.xlarge)
    p95 CPU 9% / mem 22% over 30d · est. -$412/mo
    rollback: resize back to m5.4xlarge (snapshot-free)   → L3 APPROVE

[2] Delete idle EBS  vol-07be...  (unattached 41d, gp3 500GB)
    no attachment · owner tag missing · est. -$40/mo
    guardrail: snapshot before delete, 7d retention        → L4 AUTO

[3] Increase Savings Plans coverage  compute  68% → 90%
    on-demand baseline stable 45d · 1yr no-upfront · -$3,728/mo
    rollback: n/a (commitment) — requires human sign-off    → L3 APPROVE

Apply [1] [2]? auto-applying [2] under policy. verifying...
  ✓ vol-07be snapshot snap-0c2 created, volume deleted, $40/mo confirmed
  ⧗ awaiting approval on [1] [3]  (audit: run 7f3a-c domain-brokered)

What the agent understands

Analyze Compute Engine VM, Cloud SQL, Persistent Disk, and serverless (Cloud Functions/Cloud Run) utilization to identify right-sizing opportunities. Uses Cloud Monitoring metrics with anti-hallucination rules for E2 shared-core instances, sole-tenant nodes, preemptible/spot VMs, SUD eligibility, peak vs average analysis, CUD-aware savings, and estimated monthly savings calculations.

gcp

rightsizing

Related automations

More automations in the same category.

Cloud Cost Optimization ReportGenerate a comprehensive cloud cost optimization report by analyzing spending patterns, identifying waste, and recommending savings opportunities across AWS, GCP, or Azure. Covers idle resource detection, rightsizing, reserved instance analysis, spot opportunities, and prioritized action plan.AWSMANDATORY parallel execution patterns (30x speedup), CloudWatch statistics syntax, Cost Explorer aggregation, output token limits, and common pitfalls AWS BillingAnalyze, break down, and report AWS costs and bills. Covers cost breakdown by service, account, or usage type; monthly/daily billing trends; cost anomaly detection; RI/SP utilization; cost forecasting; credit/discount analysis; and multi-account cost comparison. Uses anti-hallucination rules, mandatory currency/credit detection workflow, and reusable Cost Explorer functions.AWS Idle ResourcesDetect unused and idle AWS resources that incur cost without providing value. Covers detached EBS volumes, idle load balancers, unused Elastic IPs, stopped EC2 instances, idle NAT Gateways, old snapshots, and unused ENIs. Includes estimated monthly waste per resource and anti-hallucination rules for safe detection.AWS PricingAWS pricing helper for cost queries. ALWAYS use get_aws_cost script for pricing questions.AWS RightsizingAnalyze EC2, RDS, EBS, and Lambda resource utilization to identify right-sizing opportunities. Uses CloudWatch metrics with anti-hallucination rules for burstable instances, memory metrics, peak vs average analysis, and estimated monthly savings calculations.

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