Workflow Automation
Vharta runs Airflow DAGs, Langflow AI pipelines, and Agentic workflows on one governed platform. Jobs-as-code, GitOps deployment, headless execution — with per-tenant isolation and Policy-as-Code from day one.
Supported Engines
Bring your existing workflows. Vharta provides the governance layer without requiring you to rewrite or migrate your automations.
Execute Airflow DAGs headlessly on Kubernetes-native infrastructure. Queue-based dispatch, HPA auto-scaling (1–50 workers per tenant), and full cost attribution per DAG run.
Run Langflow AI/ML flows in production without UI dependency. Multi-provider LLM routing, per-tenant token cost tracking, and governance policies per flow.
Execute autonomous AI agent workflows with tool orchestration, multi-step reasoning, and MCP protocol integration — each tenant isolated by Kubernetes namespace.
Platform Capabilities
Every capability your platform team needs to run workflow automation at scale without becoming the ops team for three different schedulers.
Workflows live in Git. CI/CD pipelines validate schema, check license requirements, and enforce OPA policies before any workflow reaches production. No drag-and-drop UI bottleneck.
Workflows run via CLI without any UI dependency. Schedule, trigger via API, or chain across engines — Airflow DAGs can hand off to Langflow flows or Agentic tasks on the same platform.
OPA Rego policies govern which tenants can run which workflows, which LLM models are permitted, and what resource quotas apply. Policies are version-controlled and code-reviewed like any other infrastructure.
Each tenant gets a dedicated Kubernetes namespace with NetworkPolicies, ResourceQuotas, and row-level database security. Zero cross-tenant data leakage by construction.
Developer Flow
The same CI/CD workflow your engineers already use — extended with governance, license checks, and OPA policy validation before any workflow ships.
Write Airflow DAGs, Langflow flows, or Agentic configs as code in your existing repository.
Vharta's CI hooks validate schema, check license limits, and run OPA policy tests before merge.
Merging to main triggers automatic deployment to your tenant namespace — zero manual steps.
Prometheus metrics, Jaeger traces, and Loki logs give full observability. Budget alerts fire before costs overrun.
Consolidate Airflow, Langflow, and Agentic AI onto one governed platform. See a live demo with your team's actual use case.