Decision Point Component

Last updated: Jan 2026

Overview

The Decision Point component enables intelligent workflow branching based on natural language conditions. Unlike traditional if/else logic that requires exact matching, Decision Points use AI to evaluate conditions semantically, making your workflows more flexible and robust.

Simply describe the condition in plain English, and the AI will evaluate incoming data to determine which branch to take. This is particularly powerful for analyzing text sentiment, categorizing content, or making nuanced decisions that would be difficult to express with traditional boolean logic.

How It Works

1

Input Data

Data flows from the previous step into the Decision Point.

2

AI Evaluates

The AI analyzes the input against your natural language condition.

3

Branch Selection

The workflow routes to the True or False branch based on the evaluation.

When the workflow reaches a Decision Point, the AI model analyzes the input data against your natural language condition. It then routes the workflow down the appropriate branch - True if the condition is met, False otherwise.

Semantic Understanding

The AI understands context and meaning, not just keywords. "Is this message angry?" will correctly identify frustrated messages even if they don't contain the word "angry".

Configuration

Configure your Decision Point with these settings.

Condition

Write your condition in natural language. Be specific about what you're evaluating and what constitutes a "true" result.

Example Condition
Does this customer message express frustration or dissatisfaction
with our product or service?

Input Connection

Connect the Decision Point to a previous step in your workflow. The output from the connected step is automatically passed to the Decision Point for evaluation.

Condition Examples

Here are examples of effective conditions for different use cases.

Sentiment Analysis

text
Is this customer review predominantly positive, expressing satisfaction
with the product quality, delivery, or customer service?

Content Categorization

text
Does this support ticket relate to billing or payment issues
(as opposed to technical or product questions)?

Priority Detection

text
Is this request urgent based on keywords like "ASAP", "urgent",
"critical", or language indicating time sensitivity?

Quality Check

text
Does this generated content meet professional standards:
grammatically correct, coherent, and free of obvious errors?

Be Specific

The more specific your condition, the more reliable the results. Instead of "Is this good?", specify what makes something "good" in your context.

Use Cases

Decision Points are ideal for:

  • Routing customer inquiries to appropriate departments
  • Filtering content based on quality or appropriateness
  • Detecting urgent vs. routine requests
  • Categorizing feedback as positive, negative, or neutral
  • Validating AI-generated content before publishing
  • Triggering alerts for specific conditions
  • Personalizing responses based on customer context
  • Implementing approval workflows for sensitive content

Best Practices

  1. Write clear, specific conditions: Ambiguous conditions lead to unpredictable results. Define exactly what you're looking for.
  2. Test with edge cases: Try borderline examples to understand how the AI interprets your condition.
  3. Consider multi-step evaluation: For complex decisions, use multiple Decision Points in sequence.

Avoid Ambiguity

Conditions like "Is this message okay?" are too vague. Be explicit about what criteria make something "okay" in your specific context.

Troubleshooting

IssueSolution
Unexpected Branch SelectionReview your condition for ambiguity. Try rephrasing or adding more context about what constitutes a "true" result.
Inconsistent ResultsMake conditions more specific. Consider using a multi-step approach where you first categorize content before making the final decision.
Missing Input DataEnsure the Decision Point is properly connected to a previous step. Check that the connected step produces the data you're trying to evaluate.

Key Takeaways

  • Decision Points use AI to evaluate natural language conditions
  • Write specific, unambiguous conditions for reliable results
  • Test with edge cases to understand AI interpretation
  • Combine multiple Decision Points for complex logic
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