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Why Choose Kubiya?

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The Problem with Traditional Approaches

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AI Agent Frameworks: Unpredictable Chaos

  • Non-deterministic: Same input can produce different execution paths
  • Hard to debug: Complex agent interactions make troubleshooting nearly impossible
  • Production risks: Unpredictable behavior in critical environments
  • State management hell: Agents accumulate state leading to drift

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Traditional Orchestrators: Manual Complexity

  • No AI capabilities: Everything must be manually coded
  • Steep learning curve: Complex DSLs and configurations
  • Limited flexibility: Hard to adapt to changing requirements
  • Slow development: Every workflow requires extensive coding

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The Kubiya Solution

AI-Powered Generation

Natural language to workflow in seconds

Deterministic Execution

Same input always produces same result

Container Isolation

Every step runs in its own secure container

Universal Compatibility

Run any language, tool, or software

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Key Differentiators

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1. Deterministic by Design

Unlike agent frameworks where execution paths vary, Kubiya workflows follow the exact same path every time.

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2. Application Layer Security

  • No direct infrastructure access required
  • No god-mode Kubernetes permissions
  • Complete audit trail of all actions
  • Runs in isolated containers

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3. AI as a Tool, Not a Controller

# AI generates the workflow
workflow = adk.compose("Deploy my app with health checks")

# But execution is deterministic
result = execute_workflow(workflow)  # Same result every time

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4. Production-Ready from Day One

  • Built-in error handling and retries
  • Comprehensive logging and monitoring
  • Rollback capabilities
  • Health checks and validations

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Comparison Matrix

FeatureAgent FrameworksTraditional OrchestratorsKubiya
AI Capabilities✅ Deep but chaotic❌ None✅ AI-generated, deterministic
Predictability❌ Non-deterministic✅ Deterministic✅ Deterministic
Debugging❌ Nearly impossible⚠️ Complex✅ Step-by-step visibility
Setup TimeDays/WeeksDays/WeeksMinutes
Language SupportLimitedLimited✅ Universal
Production Safety❌ High risk✅ Safe but rigid✅ Safe and flexible

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Real-World Benefits

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For DevOps Teams

  • Faster automation: Natural language to production workflow in minutes
  • Reduced errors: Deterministic execution eliminates surprises
  • Easy maintenance: Clear, auditable workflow definitions
  • Universal tooling: Use any tool or language you prefer

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For Security Teams

  • No infrastructure access: Workflows run in isolated containers
  • Complete audit trails: Every action is logged and traceable
  • Least privilege: Only the permissions needed for each step
  • Compliance ready: SOC2, GDPR compliant platform

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For Development Teams

  • Rapid prototyping: Test ideas quickly with AI generation
  • Language agnostic: Use Python, Go, Node.js, or any language
  • Version control: Workflows are just code, use Git naturally
  • Testing friendly: Deterministic = easily testable

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Success Stories

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Before Kubiya

  • 🔴 3 days to build a deployment pipeline
  • 🔴 Constant debugging of agent behavior
  • 🔴 Production incidents from unpredictable AI
  • 🔴 Complex state management code

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After Kubiya

  • ✅ 30 minutes from idea to production
  • ✅ Predictable, debuggable workflows
  • ✅ Zero AI-related production incidents
  • ✅ Simple, declarative workflows

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The Bottom Line

Kubiya provides the best of both worlds:
  • AI power for rapid workflow generation
  • Deterministic execution for production reliability
  • Container isolation for security
  • Universal compatibility for flexibility

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Get Started Today

1

Install SDK

pip install kubiya-workflow-sdk
2

Generate Your First Workflow

from kubiya_workflow_sdk.providers import get_provider

adk = get_provider("adk", api_key="YOUR_KEY")
workflow = await adk.compose("Create a backup of my database")
3

Deploy with Confidence

result = execute_workflow(workflow)  # Deterministic execution

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Learn More

Platform Overview

Understand the architecture

Quickstart

Build your first workflow

Agent Comparison

Detailed framework comparison

Examples

Real-world use cases