AI-Assisted Cloud Operations
An agent that triages alerts, assembles context and proposes remediation, with human approval on every change.
What it does
Operations teams spend a large share of incident time on context gathering: which service, what changed, what does the runbook say, what do the metrics and logs show. That work is repetitive, delays resolution and is often done at night.
Rabbit Cloud's AI-Assisted Cloud Operations solution uses Amazon Bedrock Agents to perform that triage automatically. When an alert fires, the agent gathers metrics, logs, traces and recent deployments, matches the relevant runbook, writes a triage summary and proposes a remediation. Read-only investigation runs autonomously. Any action that changes infrastructure requires explicit human approval, and every step is recorded.
Built for
- First-line alert triage and enrichment
- Change and deployment correlation during incidents
- Runbook execution with approval gates
- Cost and configuration drift investigation
From input to auditable output
Intake
Alerts arrive from Amazon CloudWatch, PagerDuty or the client's existing queue.
Investigate
The agent queries metrics, logs, traces and deployment history for the affected service and time window.
Reason
Using the runbook library, the agent identifies likely causes and writes a triage summary for the on-call engineer.
Propose
A remediation is proposed with the expected effect. The engineer approves or rejects.
Execute and record
Approved actions execute through AWS Systems Manager and Step Functions. Every step is logged in AWS CloudTrail.
AWS services
Controls built in
- Read-only investigation only; no mutating action without approval
- Least-privilege execution roles scoped per runbook
- Runbooks maintained as controlled documents with review history
- Full audit trail of agent reasoning, proposals and approvals
- Works alongside existing on-call and incident tooling
How the solution is delivered
Each stage defines scope, cost and outcomes before the next commitment. Qualifying engagements are structured to AWS partner programme requirements.
| Stage | Duration | Outcome |
|---|---|---|
| Assessment | 2 to 4 weeks | Use-case scoring, data readiness review, evaluation set design and a cost-per-transaction model. |
| Proof of concept | 4 weeks | Working pipeline on the client's data against agreed success criteria, with measured accuracy and cost. |
| Production build | 8 to 16 weeks | Hardened deployment in the client's AWS account: infrastructure as code, guardrails, monitoring and integration. |
| Operate | Ongoing | Evaluation on every change, model and cost review, and managed operations under SLOs. |
Frequently asked
Will the agent take actions on its own?
No. Investigation is autonomous but read-only. Any change to infrastructure requires a named person to approve it, and that approval is recorded.
Does it replace our monitoring stack?
No. It consumes alerts from the existing stack and adds context and proposed remediation. It does not replace monitoring, alerting or incident management tools.
How is it scoped?
Runbooks and permissions are introduced incrementally, starting with the highest-volume, lowest-risk alert types identified during the assessment.
Discuss AI-Assisted Cloud Operations with a solutions architect
A structured assessment determines feasibility, data readiness and the appropriate delivery path before any commitment to build.