from kubiya_workflow_sdk.providers import get_provider

# Generate workflows with AI
adk = get_provider("adk")
result = await adk.compose(
    task="Create a zero-downtime deployment pipeline with health checks",
    mode="act"  # Instantly execute!
)

from kubiya_workflow_sdk import workflow, step

@workflow
def deploy_app():
    # Every step runs in its own container
    build = step("build").docker(
        image="docker:latest",
        command="docker build -t myapp:latest ."
    )
    
    test = step("test").docker(
        image="myapp:latest",
        command="pytest tests/"
    ).depends("build")
    
    deploy = step("deploy").docker(
        image="kubectl:latest",
        command="kubectl apply -f k8s/"
    ).depends("test")

@workflow
def smart_incident_response():
    # Embed AI decision-making in workflows
    analyze = step("analyze").inline_agent(
        message="Analyze these system metrics: ${METRICS}",
        agent_name="sre-expert",
        ai_instructions="You are an expert SRE. Identify issues and recommend fixes.",
        runners=["kubiya-hosted"],
        llm_model="gpt-4o"
    )
    
    # AI generates remediation scripts
    fix = step("auto-fix").inline_agent(
        message="Generate safe remediation for: ${ANALYSIS}",
        agent_name="remediation-expert",
        runners=["kubiya-hosted"]
    ).depends("analyze")