DevOps & SRE
SysAdmins, Cloud Engineers, DevOps & SRE organizations
LinuxKubernetesCloud APIspolicy engine
The problem
- Traditional monitoring tools — Grafana, CloudWatch, Datadog — alert you. They don't investigate, remediate, or learn.
- Incident response still depends entirely on engineers manually correlating telemetry, hunting runbooks, and executing fixes at 3am.
- Teams want more automation but are rightly wary of AI making unchecked changes to production systems.
- There's no structured model for progressively expanding AI operational authority as trust is established.
How InsightWorker handles it
InsightWorker implements a five-level autonomy model. Each level expands what the agent can do autonomously, controlled by a policy engine, approval gates, audit logs, and rollback support. Teams start at Level 0 and unlock higher levels as operational trust grows.
0
Read metrics, logs, and infrastructure state. Detect unhealthy systems and generate summaries — no changes made.
read_metrics · query_logs · describe_infra
1
Generate remediation plans and simulate changes. Every proposed action requires explicit human approval before execution.
propose_plan · simulate_change (human approval gate)
2
Autonomously execute low-risk operations — restart stateless services, rotate logs, clear temp files, scale replicas. High-risk actions (DB, IAM, firewall) still require approval.
bash · kubectl · allowlisted operations only
3
Enterprise-grade automation under governance rules — canary rollouts, automated rollback, drift detection, compliance validation, RBAC and approval chains.
policy_engine · approval_chain · audit_trail
4
Predict outage risks, optimize autoscaling, reduce cloud costs, trigger failovers, restore backups, shift regional traffic, and learn recurring incident patterns.
predictive_analysis · autoscale · failover · backup_restore
The five autonomy levels in detail
Level 0 — Visibility & Investigation
Objective: Safe, read-only operational visibility
- VM health inspection, Kubernetes visibility, log analysis
- Cloud resource visibility, backup validation, cost anomaly reporting
- Detect unhealthy systems and surface root cause candidates
Restrictions: No service restarts · No infrastructure modifications · No scaling or patching
Level 1 — Assisted Operations
Objective: AI-assisted remediation with human approval
- Generate remediation plans and corrective action recommendations
- Simulate infrastructure changes and recommend rollback procedures
- Linux troubleshooting, Kubernetes assistance, patch and cleanup planning
- Example: agent identifies oversized Docker logs and proposes cleanup — waits for approval before executing
Level 2 — Controlled Autonomous Operations
Objective: Autonomous execution for low-risk operational tasks
- Autonomous allowed: restart stateless services, restart pods, rotate logs, clear temp files, scale stateless replicas
- Approval required: database modifications, IAM changes, firewall changes, production deletions
- Required safety features: policy engine, audit logs, rollback support, simulation mode, allowlisted operations
Level 3 — Policy-Aware Infrastructure Operator
Objective: Enterprise-grade operational automation under governance rules
- Canary rollouts, automated rollback, drift detection, compliance validation
- Security operations, fleet management
- RBAC integration, approval chains, audit trails, compliance reporting
- Policies define allowed actions, approval requirements, and environment restrictions
Level 4 — Autonomous Infrastructure Management
Objective: Advanced self-healing and predictive infrastructure management
- Predict outage risks, optimize autoscaling, reduce cloud costs
- Trigger failovers, restore backups, shift regional traffic
- Learn recurring incident patterns and pre-empt failures
Recommended initial scope
Linux Operations
- Service restart
- Disk cleanup
- Log rotation
- Process investigation
Kubernetes Operations
- Pod restart
- Deployment diagnostics
- CrashLoop recovery
Cloud Operations
- Idle VM detection
- Orphaned storage cleanup
- Cloud cost analysis
Incident Operations
- Alert enrichment
- RCA generation
- Deployment correlation
- Runbook recommendations
Sample prompt
"Run a Level 0 health check across all VMs and Kubernetes pods — report unhealthy systems, disk pressure, and any pods in CrashLoopBackOff."
"We're ready to move to Level 2. Enable autonomous pod restarts and log rotation for the payments namespace — all other actions still need my approval."
Security principles: Least privilege access · Structured tooling · Full auditability · Rollback support · Simulation before execution
Deliverables: health_report.md · remediation_plan.md · audit_log.json · policy_config.yaml · incident_digest.md
Prefer the browser?
Run this in InsightStudio — no CLI install for the user.
Authors publish the app once with iw app publish;
business users open it in the marketplace and click Run. Your worker box does the execution.
Visit InsightStudio →
Try this use case yourself
Free trial available — CLI, Desktop, VS Code, and the new --worker mode for InsightStudio. See download for details.
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