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Workflows Overview

Kubiya workflows are the foundation of intelligent automation. They combine the power of AI generation with the reliability of deterministic execution, all running in containerized environments.

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The DAG Revolution

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What is a Kubiya Workflow?

A workflow in Kubiya is:

  • Directed Acyclic Graph (DAG): Steps with dependencies, no circular references
  • Container-Based: Each step runs in its own Docker container
  • Language Agnostic: Use Python, Go, Node.js, or any language
  • AI-Generated: Created from natural language or defined programmatically
  • Deterministic: Same input → Same execution → Same output

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Core Components

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1. Workflow Definition

from kubiya import workflow, step

@workflow
def my_data_pipeline():
    # Your workflow logic here
    pass

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2. Steps

Each step is an atomic unit of work:

data = step.extract(
    name="extract-data",
    image="python:3.11",
    script="extract.py",
    inputs={"source": "database"}
)

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3. Dependencies

Steps can depend on outputs from other steps:

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4. Containers

Every step runs in isolation:

🐳 Any Docker Image

Public or private registries

🔒 Complete Isolation

No shared state between steps

📦 Dependency Freedom

Each step has its own environment

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Workflow Execution Model

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Key Features

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Parallel Execution

Run independent steps simultaneously:

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Conditional Logic

Dynamic paths based on results:

if data.quality_score > 0.8:
    step.proceed_to_production()
else:
    step.alert_data_team()

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Error Handling

Built-in retry mechanisms:

step.critical_operation(
    retry=3,
    backoff="exponential",
    on_failure="alert"
)

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Inline AI Agents

Embed intelligent decision-making:

analysis = step.inline_agent(
    message="Analyze these logs for anomalies",
    runners=["kubiya-hosted"],
    tools=[log_parser_tool]
)

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Workflow vs Other Approaches

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Real-World Example

Let’s see a complete workflow that showcases Kubiya’s power:

from kubiya import workflow, step

@workflow(
    name="intelligent-deployment",
    description="AI-assisted deployment with safety checks"
)
def deploy_with_intelligence():
    # Run tests in Node.js container
    test_results = step.run_tests(
        image="node:18",
        script="npm test",
        timeout="5m"
    )
    
    # Analyze results with AI
    analysis = step.inline_agent(
        message=f"Analyze these test results: {test_results}",
        runners=["kubiya-hosted"],
        tools=[{
            "name": "decide-deployment",
            "type": "function",
            "description": "Decide if safe to deploy"
        }]
    )
    
    # Conditional deployment
    if analysis.should_deploy:
        # Build in parallel
        image = step.build_docker_image(
            dockerfile="./Dockerfile",
            tag="myapp:latest"
        )
        
        # Deploy to Kubernetes
        step.deploy_to_k8s(
            manifest="k8s/deployment.yaml",
            image=image.tag,
            namespace="production"
        )
        
        # Notify team
        step.send_notification(
            channel="deployments",
            message="🚀 Deployed successfully!"
        )
    else:
        # Alert on issues
        step.create_incident(
            severity="high",
            details=analysis.concerns
        )

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Visual Workflow Builder

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Benefits of Kubiya Workflows

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1. Predictability

  • Deterministic execution paths
  • No agent wandering or infinite loops
  • Clear audit trails

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2. Flexibility

  • Use any programming language
  • Integrate any tool or service
  • Mix AI and traditional logic

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3. Scalability

  • Parallel execution by default
  • Kubernetes-native scaling
  • Efficient resource usage

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4. Maintainability

  • Version control friendly
  • Easy to test and debug
  • Clear dependencies

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Common Patterns

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ETL Pipeline

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CI/CD Pipeline

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Data Science Pipeline

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Next Steps

Ready to build your first workflow?

Workflow Architecture

Deep dive into execution model

DSL Reference

Complete workflow syntax guide

Examples

Real-world workflow patterns

Quick Start

Build your first workflow