Building Workflows

This guide teaches you how to build powerful automated workflows that chain AI operations together. Learn to create pipelines for content generation, data processing, and complex multi-step tasks.

Understanding Workflows

What is a Workflow?

A workflow is a sequence of automated steps that process data:

Input → Step 1 → Step 2 → Step 3 → Output

Each step can:

• Call AI models
• Generate content
• Transform data
• Make API calls
• Apply conditions

When to Use Workflows

Creating Your First Workflow

Step 1: Plan Your Workflow

Before building, map out:

1. Input: What data starts the workflow?
2. Steps: What operations are needed?
3. Dependencies: Which steps depend on others?
4. Output: What's the final result?

Example Planning:

Goal: Generate blog post from topic

Input: topic (string), tone (string)

Steps:
1. Generate outline from topic
2. Write introduction from outline
3. Write body sections from outline
4. Write conclusion
5. Combine all sections

Output: Complete blog post

Step 2: Create the Workflow

1. Navigate to Workflows in the sidebar
2. Click New Workflow
3. Enter name and description:
   • Name: "Blog Post Generator"
   • Description: "Creates complete blog posts from topics"

Step 3: Define Inputs

Configure workflow inputs:

inputs:
  - name: topic
    type: string
    required: true
    description: "The blog post topic"

  - name: tone
    type: string
    required: false
    default: "professional"
    description: "Writing tone (professional, casual, technical)"

  - name: wordCount
    type: number
    required: false
    default: 1000
    description: "Target word count"

Step 4: Add Steps

Step 1: Generate Outline

step: generate_outline
type: ai_chat
config:
  model: gemini-2.0-flash
  temperature: 0.5
  prompt: |
    Create a detailed outline for a blog post about: {{input.topic}}

    Tone: {{input.tone}}
    Target length: {{input.wordCount}} words

    Include:
    - Compelling title
    - Introduction hook
    - 3-5 main sections with key points
    - Conclusion summary

    Format as structured markdown.

Step 2: Write Introduction

step: write_intro
type: ai_chat
depends_on: generate_outline
config:
  model: gemini-2.0-flash
  temperature: 0.7
  prompt: |
    Based on this outline:
    {{steps.generate_outline.output}}

    Write an engaging introduction (150-200 words) that:
    - Hooks the reader immediately
    - Introduces the topic
    - Previews what they'll learn

    Tone: {{input.tone}}

Step 3: Write Body

step: write_body
type: ai_chat
depends_on: generate_outline
config:
  model: gemini-1.5-pro
  temperature: 0.6
  prompt: |
    Based on this outline:
    {{steps.generate_outline.output}}

    Write the main body sections ({{input.wordCount * 0.7}} words).

    For each section:
    - Use clear subheadings
    - Include practical examples
    - Add relevant details

    Tone: {{input.tone}}

Step 4: Write Conclusion

step: write_conclusion
type: ai_chat
depends_on: [write_intro, write_body]
config:
  model: gemini-2.0-flash
  temperature: 0.6
  prompt: |
    Based on the introduction:
    {{steps.write_intro.output}}

    And body:
    {{steps.write_body.output}}

    Write a strong conclusion (100-150 words) that:
    - Summarizes key points
    - Provides a call to action
    - Leaves a lasting impression

Step 5: Combine

step: combine
type: transform
depends_on: [write_intro, write_body, write_conclusion]
config:
  template: |
    {{steps.write_intro.output}}

    {{steps.write_body.output}}

    {{steps.write_conclusion.output}}

Step 5: Test and Save

1. Click Test Workflow
2. Enter sample inputs:

   {
     "topic": "Remote Work Best Practices",
     "tone": "professional",
     "wordCount": 1000
   }
   

3. Review the output
4. Click Save

Workflow Patterns

Linear Pipeline

Steps execute in sequence:

[Input] → [Step 1] → [Step 2] → [Step 3] → [Output]

Use for: Simple transformations, content generation

Parallel Processing

Independent steps run simultaneously:

         ┌→ [Step 2a] ─┐
[Step 1] ├→ [Step 2b] ─┼→ [Step 3]
         └→ [Step 2c] ─┘

Use for: Multi-format output, independent generations

Conditional Branching

Different paths based on conditions:

           ┌→ [Path A] ─┐
[Step 1] ─┤             ├→ [Output]
           └→ [Path B] ─┘

Use for: Different handling based on input/results

Loop Processing

Iterate over items:

[Input List] → [Loop] → [Process Item] → [Collect Results]

Use for: Batch processing, list transformations

Step Types

AI Chat Step

Send a message to an AI model:

type: ai_chat
config:
  model: gemini-2.0-flash  # or gemini-1.5-pro
  temperature: 0.7
  systemPrompt: "You are a helpful assistant"
  prompt: "User message here"

Image Generation Step

Generate an image:

type: image_generation
config:
  model: flux-pro-1.1
  prompt: "Image description"
  aspectRatio: "16:9"
  quality: "high"

Transform Step

Modify data:

type: transform
config:
  template: "Combined: {{step1.output}} + {{step2.output}}"
  # Or use JavaScript expression
  expression: "steps.data.items.map(i => i.name).join(', ')"

HTTP Request Step

Call external APIs:

type: http_request
config:
  method: POST
  url: "https://api.example.com/webhook"
  headers:
    Authorization: "Bearer {{secrets.API_KEY}}"
    Content-Type: "application/json"
  body:
    data: "{{previousStep.output}}"

Condition Step

Branch based on logic:

type: condition
config:
  expression: "input.priority === 'high'"
  trueBranch: high_priority_step
  falseBranch: normal_step

Loop Step

Process arrays:

type: loop
config:
  items: "{{input.products}}"
  itemVariable: "product"
  step:
    type: ai_chat
    prompt: "Describe {{product.name}}"

Variables and Data Flow

Accessing Variables

Transform Expressions

Use JavaScript expressions for complex transformations:

# String manipulation
expression: "input.name.toUpperCase()"

# Array operations
expression: "steps.data.output.items.filter(i => i.active)"

# Conditional
expression: "input.premium ? 'VIP' : 'Standard'"

# JSON parsing
expression: "JSON.parse(steps.api.output).results"

Data Passing Best Practices

1. Keep data minimal - Only pass what's needed
2. Use clear names - Descriptive variable names
3. Handle null - Check for missing data
4. Format consistently - Standardize data formats

Workflow Examples

Product Description Generator

name: Product Description Generator
description: Generates product descriptions for e-commerce

inputs:
  - name: products
    type: array
    required: true
    description: Array of product objects

steps:
  - id: generate_descriptions
    type: loop
    config:
      items: "{{input.products}}"
      itemVariable: product
      step:
        type: ai_chat
        config:
          model: gemini-2.0-flash
          temperature: 0.7
          prompt: |
            Write a compelling product description for:

            Name: {{product.name}}
            Category: {{product.category}}
            Features: {{product.features}}
            Price: ${{product.price}}

            Include:
            - Attention-grabbing headline
            - Key benefits (not just features)
            - Call to action

  - id: format_output
    type: transform
    depends_on: generate_descriptions
    config:
      expression: |
        steps.generate_descriptions.output.map((desc, i) => ({
          productId: input.products[i].id,
          description: desc
        }))

Social Media Content Pipeline

name: Social Media Pipeline
description: Creates multi-platform content from a single brief

inputs:
  - name: brief
    type: string
    required: true
  - name: brand_voice
    type: string
    default: "friendly and professional"

steps:
  - id: core_message
    type: ai_chat
    config:
      prompt: |
        Extract the core marketing message from this brief:
        {{input.brief}}

        Brand voice: {{input.brand_voice}}

        Return a 1-2 sentence core message.

  - id: twitter_post
    type: ai_chat
    depends_on: core_message
    config:
      prompt: |
        Create a Twitter post (max 280 chars) for:
        {{steps.core_message.output}}

        Include relevant hashtags.

  - id: linkedin_post
    type: ai_chat
    depends_on: core_message
    config:
      prompt: |
        Create a LinkedIn post (300-500 words) for:
        {{steps.core_message.output}}

        Professional tone, include call to action.

  - id: instagram_caption
    type: ai_chat
    depends_on: core_message
    config:
      prompt: |
        Create an Instagram caption for:
        {{steps.core_message.output}}

        Engaging, include emojis, relevant hashtags.

  - id: compile_output
    type: transform
    depends_on: [twitter_post, linkedin_post, instagram_caption]
    config:
      template: |
        {
          "twitter": "{{steps.twitter_post.output}}",
          "linkedin": "{{steps.linkedin_post.output}}",
          "instagram": "{{steps.instagram_caption.output}}"
        }

Customer Support Auto-Response

name: Support Ticket Auto-Response
description: Automatically categorizes and responds to support tickets

inputs:
  - name: ticket
    type: object
    required: true
    # { subject, body, customerId }

steps:
  - id: categorize
    type: ai_chat
    config:
      temperature: 0.2
      prompt: |
        Categorize this support ticket:

        Subject: {{input.ticket.subject}}
        Body: {{input.ticket.body}}

        Categories: billing, technical, feature_request, general

        Return only the category name.

  - id: check_category
    type: condition
    depends_on: categorize
    config:
      expression: "steps.categorize.output.trim() === 'billing'"
      trueBranch: billing_response
      falseBranch: general_response

  - id: billing_response
    type: ai_chat
    config:
      systemPrompt: "You are a billing support specialist"
      prompt: |
        Write a helpful response to this billing inquiry:
        {{input.ticket.body}}

        Be empathetic and solution-focused.

  - id: general_response
    type: ai_chat
    config:
      systemPrompt: "You are a friendly support agent"
      prompt: |
        Write a helpful response to:
        {{input.ticket.body}}

        Category: {{steps.categorize.output}}

Execution and Monitoring

Triggering Workflows

Via API:

curl -X POST https://www.girardai.com/api/v1/workflows/{id}/execute \
  -H "Authorization: Bearer sk_live_xxx" \
  -d '{"inputs": {"topic": "AI"}}'

Via Webhook:

POST https://www.girardai.com/api/v1/webhooks/{workflowId}

Via Dashboard:

1. Open workflow
2. Click "Run"
3. Enter inputs
4. Execute

Monitoring Runs

Check execution status:

1. Go to workflow Runs tab
2. See all executions
3. Click run for details:
   • Step-by-step progress
   • Inputs/outputs
   • Errors and logs
   • Duration

Handling Errors

Build in error handling:

steps:
  - id: risky_step
    type: http_request
    config:
      url: "{{input.apiUrl}}"
    onError:
      retry:
        attempts: 3
        delay: 1000
      fallback: error_handler_step

  - id: error_handler_step
    type: transform
    config:
      template: |
        {
          "success": false,
          "error": "API call failed",
          "fallbackData": "Default response"
        }

Best Practices

Performance

1. Parallelize independent steps - Run together when possible
2. Use appropriate models - Flash for speed, Pro for quality
3. Limit data passing - Only pass necessary data
4. Cache when possible - Avoid redundant operations

Reliability

1. Add error handling - Retry and fallback logic
2. Validate inputs - Check data before processing
3. Log important steps - Debugging and auditing
4. Test thoroughly - Cover edge cases

Maintainability

1. Use clear names - Descriptive step IDs
2. Add comments - Document complex logic
3. Modularize - Break into smaller workflows
4. Version control - Track changes

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