What is a Workflow

A Workflow replaces the manual work of copying one agent’s output into the next prompt: define each step once, and the engine runs the full chain for you. This includes steps that wait on external services — like a web scrape or an API webhook — without you having to poll for completion.

Core Concept

Input
  ↓
[Step 1: Agent] → Output
  ↓
[Step 2: Scrape] → Output
  ↓
[Step 3: Analysis] → Output
  ↓
[Step 4: Report] → Output
  ↓
Final Result

A workflow is essentially a directed acyclic graph (DAG) where:

Nodes represent discrete work units (agent calls, API requests, data processing)
Edges represent dependencies between nodes
Variables flow between nodes via Jinja2 interpolation
Execution follows a tick-based scheduling model driven by Pub/Sub

Why Use Workflows?

Automate Complex Processes

Chain multi-step operations without manual intervention

Coordinate Multiple Agents

Have different AI agents work together on the same task

Handle Async Operations

Wait for long-running tasks (web scraping, external APIs)

Reuse & Compose

Build subworkflows and compose them into larger systems

Real-time Monitoring

Track execution progress step-by-step with detailed logs

Data Transformation

Process and enrich data through multiple stages

Workflow vs Single Agent

Single Agent Chat

User Query → Agent → Response

Direct response
Real-time only
No state persistence between calls
Linear reasoning

Workflow

User Input → [Agent 1] → [Agent 2] → [Agent 3] → Report
             ↓          ↓          ↓
          Store    Analyze    Synthesize

Multi-step orchestration
Handles async operations
Persistent state across steps
Parallel and sequential execution
Detailed monitoring per step

Key Components

1. Nodes

Individual units of work:

Agent Nodes: Call your agents with specific inputs
Data Nodes: Process or transform data
Integration Nodes: Call external APIs or scrape websites
Container Nodes: Group other nodes (sequential, parallel, loops)

2. Dependencies

Define execution order:

{
  "id": "analyze",
  "depends_on": ["scrape", "fetch"]
  // Node "analyze" only runs when both "scrape" and "fetch" complete
}

3. Variables

Share data between steps:

{{$input.topic}}              // From initial input
{{$nodes.scrape.markdown}}    // From previous node output
{{$workflow.name}}            // Workflow metadata
{{loop.item}}                 // Current loop iteration

4. Input Schema

Define what your workflow accepts:

{
  "input_schema": {
    "type": "object",
    "properties": {
      "topic": {"type": "string"},
      "depth": {"type": "integer"}
    },
    "required": ["topic"]
  }
}

Workflow Lifecycle

1. Creation

Define your workflow with nodes, dependencies, and parameters

{
  "id": "research-workflow",
  "name": "Research Workflow",
  "nodes": [...],
  "input_schema": {...}
}

2. Execution

Trigger the workflow with input data

POST /workflows/{id}/execute
{
  "input": {
    "topic": "Machine Learning"
  }
}

3. Tracking

Monitor progress in real-time

GET /usage/executions/{execution_id}

Returns current status, progress, and outputs for each step

4. Completion

Workflow either:

Succeeds: All nodes completed, output available
Fails: A node failed, workflow halted

Common Use Cases

Case 1: Market Research

Input: Company Name
  ↓
[Search] Find company info
  ↓
[Scrape] Get latest news
  ↓
[Analysis] Analyze sentiment
  ↓
[Report] Generate summary
  ↓
Output: Research Report

Case 2: Customer Support

Input: Customer Query
  ↓
[Extract] Identify intent & entities
  ↓
[Lookup] Search knowledge base
  ↓
[Generate] Create personalized response
  ↓
[Grade] Check response quality
  ↓
Output: Support Reply

Case 3: Content Creation

Input: Topic + Guidelines
  ↓
[Research] Gather information
  ↓
[Outline] Create structure
  ↓
[Draft] Write content
  ↓
[Review] Check quality
  ↓
[Polish] Final formatting
  ↓
Output: Article

Case 4: Data Pipeline

Input: Raw Data
  ↓
[Parse] Extract structured data
  ↓
[Validate] Check against schema
  ↓
[Enrich] Add computed fields
  ↓
[Store] Save to database
  ↓
[Notify] Alert completion
  ↓
Output: Process Complete

Workflow Structure

{
  "id": "workflow-unique-id",
  "name": "My Workflow",
  "description": "What this workflow does",
  "version": "1.0.0",
  "input_schema": {
    "type": "object",
    "properties": {
      "query": {"type": "string"}
    },
    "required": ["query"]
  },
  "nodes": [
    {
      "id": "step-1",
      "name": "Step 1 Name",
      "type": "plai_agent",
      "parameters": {
        "agent_name_slug": "my-agent",
        "input": "Process this: {{$input.query}}"
      }
    },
    {
      "id": "step-2",
      "name": "Step 2 Name",
      "type": "plai_agent",
      "depends_on": ["step-1"],
      "parameters": {
        "agent_name_slug": "analyzer",
        "input": "Analyze: {{$nodes.step-1.output}}"
      }
    }
  ],
  "output_definitions": {
    "final_output": {
      "node_id": "step-2",
      "description": "The final analysis"
    }
  }
}

Execution Model at a Glance

Workflows use tick-based scheduling:

Workflow is triggered via API
System publishes execution start message to Pub/Sub
Workflow engine processes one “tick” (up to 5 nodes)
Completed nodes trigger dependent nodes
Process repeats until all nodes complete or a node fails
Real-time monitoring shows progress at each step

This model enables:

Asynchronous execution - Long operations don’t block
Scalability - Multiple workflows execute concurrently
Observability - Each step is logged and tracked
Resilience - Failures are isolated to affected branches

Next Steps

Execution Engine - Understand how workflows run
Node Types - Learn about available node types
Dependencies - Define execution order
Execution Variables - Share data between steps
Input Variables - Define workflow inputs

Getting Started

Concepts

Agents

Tools

Datasources

Workflows

Jobs

Triggers

Monitor

Core Concept

Why Use Workflows?

Automate Complex Processes

Coordinate Multiple Agents

Handle Async Operations

Reuse & Compose

Real-time Monitoring

Data Transformation

Workflow vs Single Agent

Single Agent Chat

Workflow

Key Components

1. Nodes

2. Dependencies

3. Variables

4. Input Schema

Workflow Lifecycle

1. Creation

2. Execution

3. Tracking

4. Completion

Common Use Cases

Case 1: Market Research

Case 2: Customer Support

Case 3: Content Creation

Case 4: Data Pipeline

Workflow Structure

Execution Model at a Glance

Next Steps

​Core Concept

​Why Use Workflows?

Automate Complex Processes

Coordinate Multiple Agents

Handle Async Operations

Reuse & Compose

Real-time Monitoring

Data Transformation

​Workflow vs Single Agent

​Single Agent Chat

​Workflow

​Key Components

​1. Nodes

​2. Dependencies

​3. Variables

​4. Input Schema

​Workflow Lifecycle

​1. Creation

​2. Execution

​3. Tracking

​4. Completion

​Common Use Cases

​Case 1: Market Research

​Case 2: Customer Support

​Case 3: Content Creation

​Case 4: Data Pipeline

​Workflow Structure

​Execution Model at a Glance

​Next Steps

Core Concept

Why Use Workflows?

Workflow vs Single Agent

Single Agent Chat

Workflow

Key Components

1. Nodes

2. Dependencies

3. Variables

4. Input Schema

Workflow Lifecycle

1. Creation

2. Execution

3. Tracking

4. Completion

Common Use Cases

Case 1: Market Research

Case 2: Customer Support

Case 3: Content Creation

Case 4: Data Pipeline

Workflow Structure

Execution Model at a Glance

Next Steps