AI-powered cloud operations

AI Agents for Cloud Operations

Agents that monitor, investigate and troubleshoot your AWS infrastructure, and explain what they find in plain language.

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Service healthCloudWatch · logs · deploysdeploy v42p95 latencyerror rateLikely causefound · fix proposed

Where operations agents help

Monitoring
Continuous health checks across AWS resources.
Incident response
Investigate alerts and summarise what happened.
Troubleshooting
Find likely causes across services, not one dashboard at a time.
Capacity planning
Analyse usage trends and recommend scaling.
Deployment analysis
Check health after every release.
Infrastructure health
Regular health reports for every environment.
Backup verification
Confirm backups exist and meet your policy.
DR readiness
Check disaster recovery setup against your plan.

One question, every signal

  • Infrastructure health analysis
  • AWS resource inventory
  • Configuration analysis
  • Deployment verification
  • CloudWatch analysis
  • Incident investigation
  • Resource troubleshooting
  • Service dependency analysis
  • Capacity analysis
  • Scaling recommendations
  • Backup verification
  • Disaster recovery checks
  • Infrastructure health reports

“Why is my application slow?”

  • Amazon CloudWatch
  • Application Load Balancer
  • Amazon ECS
  • Amazon RDS
  • Application logs
  • Infrastructure metrics
AI operations agent
  1. Correlates the signals
  2. Identifies the possible bottleneck
  3. Explains the root cause
  4. Suggests remediation
Example workflow

AI-powered observability & incident response

The incident agent works across Amazon CloudWatch, Prometheus, Grafana, OpenSearch, Fluent Bit, AWS X-Ray (where used) and AWS CloudTrail to connect logs, metrics, traces and changes.

Capabilities: log analysis, metric analysis, alert correlation, incident summaries, root cause investigation, anomaly detection, deployment impact analysis, incident timelines, suggested remediation, post-incident analysis and automated incident reports.

  1. 1Alert
  2. 2AI incident agent starts
  3. 3Collect metrics
  4. 4Collect logs
  5. 5Check recent deployments
  6. 6Check infrastructure changes
  7. 7Correlate events
  8. 8Identify the likely cause
  9. 9Recommend action
  10. 10Human approval
  11. 11Remediation
  12. 12Verify recovery
Example workflow

AI agents for DevOps

The DevOps agent watches every release, spots problems early and recommends the safest next step.

Capabilities: CI/CD analysis, deployment troubleshooting, Terraform analysis, Kubernetes and Helm troubleshooting, Docker analysis, EKS troubleshooting, deployment health checks, infrastructure drift analysis, release analysis, automated documentation, change impact analysis and rollback recommendations.

  1. 1Git commit
  2. 2CI/CD pipeline
  3. 3Deployment
  4. 4AI DevOps agent analyses the deployment
  5. 5Monitors health
  6. 6Detects an anomaly
  7. 7Recommends rollback or remediation
  8. 8Human approval
  9. 9Executes the action
Example workflow

Technologies

  • Amazon CloudWatch
  • AWS X-Ray
  • AWS CloudTrail
  • Amazon OpenSearch Service
  • Prometheus
  • Grafana
  • Fluent Bit
  • Amazon ECS
  • Amazon EKS
  • Amazon RDS
  • Terraform
  • Helm
  • Docker
  • Amazon Bedrock

Spend less time searching dashboards during incidents

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