AI node types

Understanding the different AI node types available in Agent Builder for building AI workflows.

Preview

Agent Builder is currently in preview and may change before general availability.

Agent Builder provides AI nodes organized into categories based on the type of content they process. Combine these nodes to create sophisticated workflows.

Node categories

Text operations

Process and analyze text content

Document operations

Work with documents and files

Image operations

Analyze visual content

Data operations

Transform and enrich data

Additionally, the Custom Agent, Context Retrieval, and Intent Classification nodes provide specialized capabilities.

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Intent Classification

Available starting with FlowX.AI 5.6.0

Classify user messages and automatically route the workflow to the matching branch — combining AI classification and conditional branching in a single node.

Node type	Description	Use cases
Intent Classification	Classifies a user message into defined intents and routes to the matching branch	Chatbot routing, email triage, support ticket classification

Configuration options

• User message — Input text to classify (supports ${} references)
• Intents — Up to 10 natural language intent descriptions, each becoming an output branch
• Use memory — Include conversation history for context-aware classification
• Include rationale — Add LLM explanation for the chosen intent
• Fallback branch — Automatic “If No Intent Matches” path

Intent Classification

Learn more about configuring intents, output format, and routing behavior

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Context Retrieval

Available starting with FlowX.AI 5.6.0

Perform RAG (Retrieval-Augmented Generation) searches against a Knowledge Base and return relevant chunks — without calling an LLM.

Node type	Description	Use cases
Context Retrieval	Queries a Knowledge Base and returns matching chunks with relevance scores	RAG pipelines, document search, context gathering for downstream AI nodes

Configuration options

• Source — Knowledge Base or Memory (Memory only available in conversational workflows)
• Knowledge Base — Select which Knowledge Base to query (when source is Knowledge Base)
• User Query — the search query, supports process variable expressions
• Search type — Hybrid (default), Semantic, or Keywords
• Max Number of Chunks — how many chunks to return (1-10)
• Min Relevance Score — minimum relevance threshold (0-100%)
• Metadata Filters — structured key-value filters (AND logic) to refine results by chunk metadata
• Use advance metadata filters — toggle for expression-based filtering
• Use Re-rank — re-rank retrieved chunks before returning

Output format

The node outputs an array of retrieved chunks, each containing:

Field	Description
chunkContent	The text content of the retrieved chunk
chunkMetadata	Metadata associated with the chunk
relevanceScore	Similarity score between the query and the chunk
contentSource	The content source the chunk belongs to

Context Retrieval

Learn more about configuring Context Retrieval nodes

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Custom Agent

Create custom agents with advanced capabilities powered by Model Context Protocol (MCP) tools.

Node type	Description	Use cases
Text generation	Create text content from prompts	Reports, summaries, responses
Summarization	Condense long content	Document summaries, meeting notes
Translation	Convert between languages	Multi-language support
Document completion	Fill in templates	Form letters, contracts

Configuration options

• System prompt - Define agent behavior and constraints

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Understanding nodes

Understanding nodes analyze content to extract meaning and intent.

Node type	Description	Use cases
Sentiment analysis	Detect emotional tone	Customer feedback, reviews
Topic modeling	Identify themes and subjects	Document categorization
Intent recognition	Understand user goals	Chatbot routing, request handling
Named entity recognition	Find people, places, organizations	Data extraction, compliance

Configuration options

• Classification labels - Define categories for classification
• Confidence threshold - Minimum score for results
• Multi-label - Allow multiple classifications per input

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AI Document Operations

Process documents to extract data, generate reports, or understand content.

Node	Description	Use cases
Document Generation	Automatically build reports or complete templates based on given inputs	Report generation, template completion
Document Extraction	Identify and extract structured data, entities or metadata from documents	Form processing, invoice data extraction
Document Understanding	Analyze documents to extract meaning, topics, sentiment, or important information	Document classification, content analysis
Extract Data from File	Extract text and data from documents and images with configurable extraction strategies	OCR, PDF text extraction, image data extraction, signature detection

Extract Data from File

Learn more about configuring extraction strategies, image extraction, and signature detection

Configuration options

• Document type - Specify expected document format
• Schema definition - Define expected output structure
• Field mapping - Map extracted fields to data model
• Confidence threshold - Minimum score for extractions

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AI Image Operations

Analyze visual content to generate captions, extract details, or identify objects.

Node type	Description	Use cases
Object recognition	Identify items in images	Document classification, damage assessment
Text extraction (OCR)	Read text from images	Invoice processing, ID verification
Scene understanding	Interpret image context	Property assessment, claims processing
Emotion analysis	Detect facial expressions	Customer experience, fraud detection

Configuration options

• Detection confidence - Minimum threshold for detections
• Region of interest - Focus on specific image areas
• Output format - Structured data or annotations

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Combining nodes

Nodes can be connected in workflows to create complex processing pipelines:

Document Input → Text Extraction → Entity Recognition → Validation → Output

Best practices

Start simple

Begin with a single node and add complexity incrementally. Test each addition before moving on.

Use validation nodes

Add validation steps after extraction to ensure data quality before processing continues.

Handle errors

Include fallback paths for when nodes fail or return low-confidence results.

Monitor performance

Track execution times and accuracy metrics to identify bottlenecks and improvement opportunities.

Related resources

Agent Builder overview

Get started with Agent Builder

Use cases

See real-world examples

Conversational workflows

Multi-turn chat with Custom Agent node changes for chat replies and memory