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The Platform Engineering Mindset

Modern engineering teams need automation that scales with their organization. Kubiya applies platform engineering principles to AI automation, creating a foundation that:
  • Reduces cognitive load for developers and operators
  • Standardizes operations across teams and environments
  • Maintains reliability even as systems grow in complexity
  • Enables self-service without compromising security

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Kubiya vs. Traditional Approaches

  • Pure AI Agents
  • Rigid Automation
  • Kubiya's Approach
Challenges with Autonomous AI
  • Unpredictable outputs for the same inputs
  • Difficulty maintaining context over long operations
  • Hard to debug when things go wrong
  • Security risks from uncontrolled system access
  • No clear audit trail for compliance
Example: An AI agent asked to “scale the production cluster” might:
  • Scale the wrong service
  • Use outdated configuration
  • Miss dependencies between services
  • Provide no rollback mechanism

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Core Differentiators

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🎯 Deterministic by Design

# Every execution follows the same logical path
workflow:
  name: "scale-cluster"
  steps:
    - check_current_capacity
    - validate_resource_limits  
    - update_replica_count
    - verify_health_checks
    - send_notification

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🔒 Security First

  • Container Isolation: Every operation runs in ephemeral, isolated environments
  • Least Privilege: Fine-grained permissions for each workflow step
  • Audit Everything: Complete activity logs for compliance and debugging
  • Policy Enforcement: Define what actions are allowed for different users/teams

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🔧 Self-Healing Integrations

Unlike static API clients, Kubiya’s integrations:
  • Automatically discover API changes and adapt
  • Provide consistent interfaces across tool versions
  • Handle authentication and credential rotation
  • Offer unified error handling and retry logic

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🌐 Multi-Environment Operations

Orchestrate operations across:
  • Multiple Kubernetes clusters
  • Different cloud providers
  • Development, staging, and production environments
  • On-premises and cloud-native services
Multi-environment context visualization

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Real-World Impact

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Before Kubiya

  • 2 hours to investigate and resolve a typical incident
  • 15+ tools to check during troubleshooting
  • Multiple team members needed for complex deployments
  • Inconsistent processes between team members
  • Weekend escalations for “simple” operations
  • 30% of time spent maintaining automation scripts
  • Frequent breakages when APIs change
  • Difficult to onboard new team members
  • Limited visibility into what automation is doing
  • Security reviews required for every script change

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After Kubiya

  • 15 minutes to resolve incidents with AI-guided workflows
  • Single interface provides all necessary context
  • Self-service operations for the entire engineering team
  • Consistent, auditable processes every time
  • Automated incident response outside business hours
  • 80% reduction in maintenance overhead
  • Self-healing integrations adapt to API changes
  • New workflows created through natural language
  • Complete observability into all automation
  • Policy-based security without review bottlenecks

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Built for Your Stack

Kubiya integrates with the tools you already use:

Cloud Platforms

AWS, Azure, GCP, DigitalOcean, and more

Container Orchestration

Kubernetes, Docker, OpenShift, Rancher

CI/CD Pipelines

GitHub Actions, GitLab CI, Jenkins, CircleCI

Monitoring & Observability

Datadog, New Relic, Grafana, Prometheus

Communication

Slack, Microsoft Teams, Discord, PagerDuty

Infrastructure as Code

Terraform, Pulumi, CloudFormation, Ansible
Ready to transform your automation? Start with our quickstart guide or explore how integrations make it all possible.
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