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Ankra’s AI Assistant analyzes your cluster’s real-time state—pods, events, logs, and configurations—to diagnose issues and provide actionable solutions without leaving the platform.

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What is AI Troubleshooting?

AI Troubleshooting is an integrated assistant that helps you debug Kubernetes issues by:
  • Analyzing live cluster data - Pods, events, logs, nodes, and resource configurations
  • Identifying root causes - Not just symptoms, but why problems occur
  • Providing actionable steps - Direct links to fix issues in the Ankra UI
  • Maintaining context - Follow-up questions understand your conversation history

Intelligent Analysis

AI understands Kubernetes patterns like CrashLoopBackOff, ImagePullBackOff, OOMKilled, and provides targeted solutions.

Real-Time Data

Fetches current pod status, container logs, events, and node conditions for accurate diagnosis.

Context Aware

Knows what resource you’re viewing and adapts responses accordingly—no need to repeat context.

Platform Integrated

Provides clickable links to Ankra UI pages instead of kubectl commands.

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How It Works

When you ask a question, the AI Assistant:

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1. Intelligent Resource Planning

The AI first determines what information to gather based on your question:
Question TypeResources Fetched
”Why is my pod crashing?”Pod status, container logs, events
”Are all deployments healthy?”Deployments, pods, replica status
”What’s wrong with ingress?”Ingress config, services, endpoints
”Node issues”Node conditions, capacity, pod distribution

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2. Data Collection

Based on the plan, Ankra fetches:
  • Pod information - Phase, restart count, container states, conditions
  • Events - Warning events, scheduling failures, image pull errors
  • Logs - Container stdout/stderr (last 50 lines by default)
  • Node status - Ready conditions, capacity, allocatable resources
  • Related resources - Deployments, services, configmaps as needed

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3. AI Analysis

Claude AI analyzes the collected data to:
  • Identify the specific issue (e.g., exit code 137 = OOMKilled)
  • Explain the root cause (e.g., memory limit too low for workload)
  • Assess severity (critical, warning, info)
  • Suggest fixes with direct Ankra UI links

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Accessing AI Troubleshooting

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AI Incidents (Alert-Triggered Analysis)

When alerts trigger, AI analysis results appear in: Alerts → AI Incidents tab This shows all automatically generated analyses with root cause, affected resources, and recommended actions. Learn more about AI Incidents.

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Global AI Assistant (On-Demand)

Press ⌘ + I (Mac) or Ctrl + I (Windows/Linux) to open the AI Assistant from anywhere in the platform for on-demand troubleshooting.

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Resource Detail Pages

When viewing a specific resource (pod, deployment, etc.), the AI Assistant automatically knows what you’re looking at:
  • Pod Details → Ask “Why is this crashing?” without specifying the pod name
  • Logs Tab → Ask “What do these errors mean?”
  • Events Tab → Ask “What caused these warnings?”

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Dedicated Troubleshooting Page

Navigate to Cluster → Troubleshooting for a full-screen AI chat experience with conversation history.

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Example Questions

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Pod Issues

Why is my nginx-pod-xyz crashing?
The AI will analyze:
  • Container exit codes and restart counts
  • Recent warning events
  • Container logs for error messages
  • Memory/CPU limits vs actual usage

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Cluster Health

Are all pods healthy?
Response format: 
Pod Health Summary: 43/46 pods healthy (3 with issues)

Pods with Issues:
- web-services/api-server-xyz: CrashLoopBackOff (11 restarts)
- monitoring/prometheus-abc: ImagePullBackOff
- database/postgres-def: Pending (insufficient memory)

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Resource Counting

How many namespaces are in my cluster?
The AI provides cluster-wide data, regardless of what page you’re on.

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Debugging Specific Issues

Why is the customer-api deployment not starting?
The AI traces through:
  1. Deployment replica status
  2. ReplicaSet events
  3. Pod scheduling attempts
  4. Container startup errors

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Add-on Troubleshooting

For Helm add-ons, the AI provides specialized analysis:
When troubleshooting add-ons, the AI also checks:
  • ArgoCD sync status - OutOfSync, Degraded, Healthy
  • Helm release state - Deployed, Failed, Pending
  • Configuration values - Misconfigurations in values.yaml
  • Latest job results - Installation/update failures
  • CRD dependencies - Missing Custom Resource Definitions
Example:
Why did cert-manager fail to install?
Response includes:
  • Helm chart version compatibility
  • Missing CRDs or prerequisites
  • RBAC permission issues
  • Specific error from the Helm job

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Common Failure Patterns

The AI recognizes and explains these Kubernetes patterns:
PatternCauseAI Diagnosis
CrashLoopBackOffApp exits with errorAnalyzes logs for exit code and error messages
ImagePullBackOffCan’t pull container imageChecks image name, registry, and credentials
PendingCan’t schedule podReviews node resources, taints, tolerations
OOMKilledOut of memoryCompares limits vs actual usage
EvictedNode under pressureChecks node conditions and pod priority
CreateContainerErrorContainer config issueExamines volume mounts, secrets, configmaps

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Response Format

AI responses follow a consistent structure:

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Problem Summary

Brief overview of what’s happening.

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Root Cause

Technical explanation of why it’s occurring, with specific details from logs/events.

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Impact Assessment

Severity indicator:
  • 🔴 Critical - Service down, data loss risk
  • ⚠️ Warning - Degraded performance, needs attention
  • ℹ️ Info - Informational, no action needed

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Suggested Actions

Numbered steps with direct links to Ankra UI:
  1. View pod logs at Pod Logs
  2. Check resource limits in pod configuration
  3. Update memory limit to 512Mi
  4. Restart the deployment
The AI prioritizes Ankra UI actions over kubectl commands. You can fix most issues directly in the platform.

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Conversation Context

The AI maintains context throughout your session: 
You: Why is the api-server pod failing?

AI: The api-server-xyz pod is in CrashLoopBackOff due to missing 
    DATABASE_URL environment variable...

You: How do I add that variable?

AI: Since you're using the api-server deployment, you can add the 
    environment variable in the pod configuration:
    1. Navigate to the deployment at [link]
    2. Edit the container spec...
Follow-up questions reference the previous context automatically.

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Stack-Based Fixes

When the solution requires creating Kubernetes resources, the AI guides you through Stack-based creation:
Benefits of Stack-based resource creation:
  • GitOps workflow - Version controlled, auditable changes
  • Declarative management - Resources defined as code
  • Rollback capability - Easy to revert if needed
  • Dependency tracking - Resources managed alongside related manifests
Instead of:
kubectl create secret generic db-creds --from-literal=password=xxx
The AI suggests:
  1. Navigate to Stacks page
  2. Create a new Stack or edit existing
  3. Add the Secret manifest
  4. Deploy the stack

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Tips for Best Results

Be specific - “Why is nginx-pod-xyz crashing?” works better than “pods not working”
Use context - When on a resource detail page, ask “What’s wrong with this?” instead of repeating the name
Follow up - Ask clarifying questions like “How do I fix that?” or “Show me the logs”
Include logs - For crash issues, the AI automatically fetches logs, but you can specify “include logs” for other queries

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Privacy & Data

  • AI analysis happens on Ankra’s secure infrastructure
  • Logs and configurations are processed in real-time, not stored for AI training
  • Conversation history is saved per-cluster for your convenience
  • You can start a new conversation at any time to clear context
Need help? Join our Slack community for support.