Which aspects of cloud infrastructure optimization and management can be effectively handled by agentic AI workflows?

1. Resource Provisioning and Scaling

Dynamic Autoscaling: Agents can monitor workload patterns and automatically adjust compute, storage, and network resources in real time.

Predictive Scaling: Using historical data and AI forecasting, agents can pre-emptively scale resources before demand spikes.



2. Cost Optimisation

Rightsizing Instances: Agents analyse utilisation metrics and recommend or execute downsizing/upgrading of VMs or containers.

Spot Instance Management: Automatically switch workloads to cheaper spot/preemptible instances when available.

Idle Resource Clean-up: Detect and decommission unused resources (e.g., orphaned volumes, idle load balancers).



3. Performance Monitoring & Self-Healing

Anomaly Detection: AI agents can identify latency spikes, CPU bottlenecks, or network congestion and trigger corrective actions.

Automated Remediation: Restart failed services, re-route traffic, or provision additional nodes without human intervention (test it first in Dev and UAT (for high and critical services (within a CI/CD pipeline) before Production Deployment)



4. Security & Compliance

Continuous Compliance Checks: Agents enforce policies (e.g., encryption, IAM roles) and remediate violations automatically.

Threat Response: Detect suspicious activity and isolate compromised resources or rotate credentials autonomously.



5. Multi-Cloud & Hybrid Orchestration

Workload Placement Optimization: Agents decide where to run workloads based on cost, latency, and compliance requirements.

Cross-Cloud Failover: Automatically migrate workloads during outages or performance degradation.



6. Observability & Reporting

Intelligent Dashboards: Agents aggregate telemetry and generate actionable insights.

Root Cause Analysis: AI-driven correlation of logs, metrics, and traces to pinpoint issues faster.



7. Policy-Driven Governance

Automated Enforcement: Apply tagging, resource quotas, and access controls consistently across environments.

Drift Detection: Identify and correct configuration drift from desired state.



Why Agentic AI is Ideal Here

Autonomy: Reduces manual intervention for repetitive tasks.

Adaptability: Responds to dynamic workloads and changing conditions.

Proactivity: Predicts issues before they impact performance or cost.

I don't see a reason why Cloud infra optimization and management can be done through agentic AI workflows, however you might want to start with less critical applications since the runtime modification without human in the loop could introduce risk for mission critical apps.

What do you mean with Handled? At the moment I would say most tools can help with insights and recommendations but I would not recommend a full autonomous handover unless you have a solid foolproof business logic to guide it, in which case you could automate most tasks anyway.

Off the top of my head I would think it has to do with what training data you have available will determine what can be handled by agentic AI. For instance, auto-scaling. If you did not have much by way of historical data with which to train a model, this is something that would still be approachable. Depending on your business model, with as little as 2 weeks of data you could let agentic AI auto-scale your compute. However, I have done some auto-scaling previously and it is not as simple as you might expect.

It could be argued that infrastructure is "too foundational" to trust to agentic AI.