Workflows are sequences of automated steps that accomplish complex tasks safely and reliably using containers.

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What workflows do

Run steps in containers: Each step runs in its own Docker container with specific tools and dependencies. Connect step outputs: Pass data between steps to build complex automation sequences. Handle failures gracefully: Built-in retry logic and error handling keep workflows robust. Execute in parallel: Independent steps run simultaneously for faster completion. Integrate AI agents: Include intelligent decision-making within deterministic workflows.

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Basic workflow structure

from kubiya_workflow_sdk.dsl import workflow

# Define a deployment workflow
deploy_workflow = (
    workflow("deploy-application")
    .description("Test, build, and deploy application")
    
    # Step 1: Run tests
    .step("test", "npm test")
    
    # Step 2: Build Docker image  
    .step("build", "docker build -t myapp:latest .")
    
    # Step 3: Deploy to Kubernetes
    .step("deploy", "kubectl apply -f k8s/deployment.yaml")
)

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Key benefits

Reliable: Same inputs produce same outputs every time - no surprises in production. Isolated: Each step runs in its own container, preventing conflicts and dependency issues. Language-agnostic: Use Python, Node.js, Go, or any language/tool you need. AI-enhanced: Include intelligent agents that can make decisions and use tools. Scalable: Steps run in parallel when possible, and the system scales automatically.

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Common workflow patterns

Deployment pipeline: Tests → Build → Deploy → Verify Data processing: Extract → Transform → Validate → Load Incident response: Detect → Analyze → Execute fix → Notify team Code review: Fetch changes → AI analysis → Post feedback

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Container execution

Every step runs in its own container:
  • Use any Docker image (Python, Node.js, custom tools)
  • No shared state between steps
  • Clean environment for each operation
  • Automatic resource management
# Different containers for different tasks
step.analyze(image="python:3.11", script="analyze.py")
step.build(image="node:18", command="npm run build") 
step.deploy(image="kubectl:latest", command="kubectl apply -f app.yaml")

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AI integration

Include intelligent agents in workflows: 
# AI agent analyzes logs and decides next action
analysis = step.inline_agent(
    message="Analyze these error logs and suggest fixes",
    tools=[log_parser_tool, fix_generator_tool]
)

# Use AI decision in next step
if analysis.severity == "high":
    step.emergency_response()
else:
    step.create_ticket()

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Error handling

Built-in resilience patterns: 
step.fetch_data(
    retry=3,                    # Retry failed steps
    timeout="5m",              # Set time limits
    on_failure="send_alert"    # Handle failures
)

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Getting started

Define workflow: Write steps and their dependencies Test locally: Run workflows in development environment Deploy to production: Execute on your infrastructure with runners Monitor execution: Track progress and debug issues
Next: Try building your first workflow with our examples or quick start guide.