Unlike unpredictable agent-based systems, Kubiya workflows are deterministic by design . Same input → Same execution path → Same output, every time.
The Problem with Non-Deterministic Systems Traditional Agent Systems Problems:
🎲 Different execution paths each time
🐛 Impossible to debug failures
📈 Errors compound through the chain
⏱️ Unpredictable execution time
🔍 No clear audit trail
Kubiya’s Deterministic Approach Benefits:
✅ Same execution path every time
✅ Clear debugging with step-by-step logs
✅ Isolated failures don’t cascade
✅ Predictable performance
✅ Complete audit trail
What Makes Kubiya Deterministic? 1. DAG-Based Execution Workflows are Directed Acyclic Graphs (DAGs) :
workflow = ( Workflow( "deploy-app" ) .step( "validate" , "validate.sh" ) .step( "build" , "docker build -t app:latest ." , depends_on = [ "validate" ]) .step( "test" , "pytest tests/" , depends_on = [ "build" ]) .step( "deploy" , "kubectl apply -f k8s/" , depends_on = [ "test" ]) ) Why DAGs?
No circular dependencies
Clear execution order
Parallelizable where possible
Stateless between runs
2. Isolated Step Execution Each step runs in an isolated container:
# Step execution apiVersion : batch/v1 kind : Job metadata : name : workflow-${id}-step-${name} spec : template : spec : containers : - name : step image : ${step.image} command : ${step.command} env : ${step.env} restartPolicy : Never Isolation Benefits:
No side effects between steps
Clean environment each time
Resource limits enforced
Security boundaries maintained
3. Immutable Workflow Definitions Once defined, workflows don’t change during execution:
# Workflow is compiled to immutable JSON { "name" : "deploy-app" , "version" : "1.0.0" , "steps" : [ { "name" : "validate" , "command" : "validate.sh" , "hash" : "abc123..." # Content-addressed } ] } 4. Explicit Dependencies Dependencies are declared, not discovered:
# Clear dependency graph .step( "fetch-data" , "wget data.csv" ) .step( "process" , "python process.py" , depends_on = [ "fetch-data" ]) .step( "upload" , "aws s3 cp output.csv s3://bucket/" , depends_on = [ "process" ]) Comparison with Other Systems Feature Kubiya LangChain/Agents Traditional CI/CD Step Functions Deterministic ✅ Always ❌ Varies ✅ Yes ✅ Yes Debuggable ✅ Step-by-step ❌ Black box ⚠️ Limited ⚠️ Limited Scalable ✅ K8s native ❌ Single process ⚠️ Runner limits ✅ AWS only Portable ✅ Any K8s ❌ Python only ❌ Platform specific ❌ AWS only AI Integration ✅ Optional ✅ Core ❌ None ❌ None
Determinism in Practice Example: Database Backup Workflow backup_workflow = ( Workflow( "db-backup" ) # Always executes in this order .step( "check-space" , "df -h /backup" ) .step( "dump-db" , "pg_dump -Fc mydb > backup.sql" , depends_on = [ "check-space" ]) .step( "compress" , "gzip backup.sql" , depends_on = [ "dump-db" ]) .step( "upload" , "aws s3 cp backup.sql.gz s3://backups/" , depends_on = [ "compress" ]) .step( "verify" , "aws s3 ls s3://backups/backup.sql.gz" , depends_on = [ "upload" ]) .step( "cleanup" , "rm backup.sql.gz" , depends_on = [ "verify" ]) ) Guarantees:
Space is always checked first
Database is only dumped if space available
Upload only happens after successful compression
Cleanup only after verification
Same execution order every time
Handling Non-Deterministic Operations Even with external services, maintain determinism:
# Deterministic retry logic .step( "api-call" , command = "curl https://api.example.com/data" , retry = RetryPolicy( max_attempts = 3 , backoff = "exponential" , max_delay = 60 ) ) AI Integration Without Chaos When using AI providers, the workflow structure remains deterministic:
# AI generates the workflow, but execution is deterministic adk = get_provider( "adk" ) workflow = adk.generate_workflow( "backup databases" ) # The generated workflow is a fixed DAG print (workflow) # { # "name": "backup-databases", # "steps": [ # {"name": "check-disk", "command": "df -h"}, # {"name": "dump-postgres", "command": "pg_dump..."}, # ... # ] # } # Execution is 100% predictable client.execute_workflow(workflow) Benefits for Production 1. Reliable Deployments deploy_workflow = ( Workflow( "production-deploy" ) .step( "run-tests" , "pytest" ) .step( "build" , "docker build" , depends_on = [ "run-tests" ]) .step( "push" , "docker push" , depends_on = [ "build" ]) .step( "deploy" , "kubectl rollout" , depends_on = [ "push" ]) .step( "verify" , "curl health-check" , depends_on = [ "deploy" ]) ) # This ALWAYS follows the same path # No surprises in production 2. Debugging Failures # When something fails, you know exactly where $ kubiya logs workflow-123 Step: run-tests [SUCCESS] 2.3s Step: build [SUCCESS] 45.2s Step: push [FAILED] 12.1s Error: Registry timeout Retry 1/3... 3. Compliance and Auditing // Complete execution history { "workflow" : "financial-report" , "execution_id" : "exec-789" , "steps" : [ { "name" : "fetch-transactions" , "started" : "2024-01-15T10:00:00Z" , "completed" : "2024-01-15T10:00:45Z" , "status" : "success" , "output_hash" : "sha256:abc..." } ] } Best Practices for Deterministic Workflows The Bottom Line
Predictable Same workflow, same execution, every time
Debuggable Clear visibility into every step
Auditable Complete execution history
Scalable Determinism enables parallelization
What’s Next? 