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Workflows

Build and execute DAG-based AI pipelines on Kubernetes. Design workflows conversationally with the STAN AI assistant or using the visual node editor. Chain together 255+ nodes across data sources, transformations, AI models, agents, control flow, and destinations -- with full execution tracing and real-time monitoring.

What are Workflows?​

Workflows are directed acyclic graph (DAG) pipelines that execute on Kubernetes. Each workflow is composed of interconnected nodes that define a data processing pipeline. Workflows can be built in two ways:

  • STAN AI Assistant: A conversational AI agent that builds workflows through natural language. STAN understands the full node catalog, can configure nodes, wire connections, and iterate on designs interactively.
  • Visual Node Editor: A drag-and-drop canvas interface for manually assembling and configuring workflow nodes.

Each workflow consists of:

  • Trigger: The event that initiates the workflow (11 trigger types available)
  • Processing Nodes: Components that transform, analyze, route, or act on data
  • Destinations: Where processed data is delivered

Architecture​

Workflows run on a Kubernetes-native execution engine with the following characteristics:

  • DAG Execution: Nodes execute in dependency order with automatic parallelization of independent branches.
  • Fast Execution: Dedicated compute resources are pre-provisioned per organization for low-latency execution dispatch.
  • STRONGLY_SERVICES: A dynamic service discovery mechanism that provides workflow nodes with access to platform services (AI models, databases, add-ons). The configuration is injected as a JSON environment variable so nodes can connect to user-selected services at runtime.
  • Automatic Retry: Failed executions can be automatically retried.
  • Distributed Execution: Workflows can execute across multiple zones for parallel processing and fault tolerance.

Node Catalog​

The platform provides 255 nodes across 11 categories:

CategoryCountExamples
Sources121PostgreSQL, MySQL, MongoDB, S3, Salesforce, Jira, Slack, Google Sheets, REST API, and 112 more
Transform33Code, AI Transform, Filter, Aggregate, PDF Parser, Excel Parser, Text Chunker, and 26 more
Destinations23PostgreSQL, MongoDB, S3, Slack, Email (SMTP/SendGrid), Teams, Webhook Response, and 16 more
Control Flow20Conditional, Loop, Map, Parallel Branch, Switch/Case, Sub-Workflow, Human Checkpoint, and 13 more
Agents15ReAct Agent, Multi-Agent Chat, RAG Agent, Supervisor Agent, Planner, Reflection, and 9 more
Triggers11Webhook, Schedule, REST API, Chat, Email, Form, File, RSS Feed, SSE, Error, Multi-Modal Input
Evaluation10LLM as Judge, Faithfulness Checker, RAG Metrics, Guardrails, Cost Tracker, and 5 more
Memory8Conversation Memory, Knowledge Base, Semantic Memory, Working Memory, and 4 more
AI7AI Gateway, LLM, Embeddings, Vision, Image Generation, Speech-to-Text, Text-to-Speech
Tools6Web Search, Code Interpreter, Web Browser, File Manager, Calculator, API Caller
Operators1MCP Tools Provider (connects to external MCP servers for tool access)

Trigger Types​

Workflows support 11 trigger types:

TriggerNode IDDescription
WebhookwebhookHTTP requests with HMAC signature verification
SchedulescheduleTime-based execution via Kubernetes CronJobs (intervals, daily, cron expressions)
REST APIrest-api-triggerAuthenticated API endpoints
Chatchat-triggerConversational input for chat-based workflows
Emailemail-triggerIncoming email processing
FormformForm submissions with validation
Filefile-triggerFile upload or file system events
RSS Feedrss-triggerRSS/Atom feed polling
SSEsse-triggerServer-Sent Events stream
Errorerror-triggerError handling and recovery flows
Multi-Modal Inputmulti-modal-inputCombined text, image, audio, and file input

Execution Modes​

Workflows can be executed in several modes:

  • Manual: On-demand execution from the UI or API.
  • Scheduled: Recurring execution via Kubernetes CronJobs with cron expression configuration.
  • Webhook/API Triggered: External systems invoke workflows via HTTP endpoints.
  • Dedicated Compute: Executions run on pre-provisioned resources, reducing cold-start latency.
  • Distributed: Multi-zone parallel processing for high-throughput workloads.

Key Features​

STAN AI Assistant​

STAN is a conversational AI agent that builds and configures workflows through natural language interaction. STAN operates in two modes:

  • NEW mode: Creates workflows from scratch based on requirements described in plain language.
  • ACTIVE mode: Modifies and iterates on an existing workflow loaded in the editor.

STAN has access to the full node catalog, can configure node parameters, establish connections between nodes, and apply workflow templates. It uses MCP (Model Context Protocol) tools to interact with the workflow builder programmatically.

Visual Node Editor​

  • Drag-and-drop interface for assembling workflow nodes
  • Real-time execution visualization on the canvas
  • Node connections define data flow between components
  • Canvas organization, grouping, and layout tools

Execution Infrastructure​

Each organization has dedicated compute resources for workflow execution:

  • Pre-provisioned resources reduce execution start time.
  • Automatic scaling adjusts capacity based on demand.
  • Resource reuse returns compute resources after execution completes, enabling efficient utilization.

Execution Tracing​

Every workflow execution produces detailed tracing data:

  • Per-node spans with timing, status, inputs, and outputs tracked per node.
  • Real-time progress reporting with status updates as each node completes.
  • Execution timeline with node durations for performance analysis.
  • Console logs and error messages captured per node.
  • Performance metrics including success rate, duration, and throughput.

Versioning​

  • Integer-based version numbers that auto-increment on each save.
  • Each deployment creates a versioned snapshot of the workflow definition.
  • Version history is maintained for audit and comparison.

Sharing and Collaboration​

  • Share workflows with specific users within an organization.
  • Clone existing workflows to create copies.
  • Create and apply workflow templates for common patterns.

STRONGLY_SERVICES​

STRONGLY_SERVICES is a JSON configuration injected into workflow workers that provides dynamic access to platform services:

  • AI Models: Users select which AI Gateway models to use; the selected model configuration is available to AI nodes at runtime.
  • Databases: Connection details for PostgreSQL, MySQL, MongoDB, and other data sources.
  • Add-ons: Third-party service integrations configured at the platform level.

Multiple services of the same type can be configured, and users select which service each node should use during workflow configuration.

S3 Intelligent Caching​

  • L1 Cache: In-memory caching for frequently accessed data within a single execution.
  • L2 Cache: Disk-based caching for larger datasets that persist across node executions.

Human Checkpoints​

Workflows can include human checkpoint nodes that pause execution and wait for manual approval before continuing. This enables review-and-approve patterns for sensitive operations.

Automatic Retry​

Failed executions can be:

  • Inspected for failure details and error context.
  • Retried automatically based on configured retry settings.
  • Retried manually after the underlying issue is resolved.

Monitoring and Observability​

Every workflow execution provides:

  • Real-time status updates via reactive subscriptions
  • Execution timeline with per-node durations
  • Input/output data inspection for each node
  • Console logs and error messages
  • Performance metrics (success rate, average duration, throughput)
  • Resource utilization and availability monitoring

Use Cases​

Workflows are designed for:

  • Data Processing Pipelines: Extract, transform, and load data from 121+ source connectors into 23+ destinations.
  • AI-Powered Automation: Chain AI models, agents, and evaluation nodes for complex analysis and decision-making.
  • Agentic Workflows: Build multi-agent systems with ReAct, RAG, and Supervisor agents that use tools and memory.
  • Event-Driven Integrations: React to webhooks, emails, RSS feeds, SSE streams, and file events.
  • Scheduled Tasks: Run recurring jobs, data synchronization, and report generation via CronJobs.
  • Document Analysis: Parse PDFs, Excel files, Word documents, and emails, then process with AI.
  • Multi-System Integration: Connect databases, APIs, cloud services, and SaaS platforms in a single pipeline.
  • Chat Applications: Build conversational workflows with chat triggers, memory, and AI agents.
Best Practice

Start with simple workflows and gradually add complexity. Use STAN AI assistant to quickly scaffold workflows, then fine-tune in the visual editor. Test thoroughly before deploying to production.

Getting Started​