Agents Hub
Build intelligent, conversational AI assistants that understand context, use tools, and complete tasks - all through AgenticFlow's intuitive visual interface. No coding required.
What are AI Agents?
AI Agents are interactive assistants that combine the power of large language models with real-world capabilities. Think of them as AI team members that can:
Converse Naturally - Chat with users in their own words, understanding context and nuance
Access Knowledge - Search through your documents, databases, and knowledge bases instantly
Use Tools - Connect to 300+ integrations including CRMs, databases, APIs, and business systems
Take Action - Execute workflows, send messages, create records, and automate tasks
Remember Everything - Maintain conversation history and context across multiple interactions
Handle Multiple Formats - Process text, images, audio, video, and documents
Unlike simple chatbots with scripted responses, AgenticFlow agents think, reason, and make decisions to solve problems.
When to Use Agents
Perfect for Interactive Assistance
Customer Service & Support
Answer customer questions 24/7
Look up order status and account information
Escalate complex issues to human agents
Provide personalized recommendations
Sales & Lead Qualification
Engage prospects in real-time conversations
Qualify leads with intelligent questioning
Schedule demos and meetings
Provide product information
Internal Knowledge Assistants
Answer employee questions from company knowledge bases
Onboard new team members
Provide policy and procedure guidance
Research and summarize information
Personal Assistants
Schedule appointments and manage calendars
Send reminders and follow-ups
Draft emails and documents
Organize tasks and projects
When to Use Other Tools
Use Workflows Instead When:
You need scheduled, automated processes (e.g., daily reports)
Tasks run without human interaction
You're processing batches of data
The sequence of steps is fixed and doesn't require decisions
Use Workforce Instead When:
Multiple specialized agents need to collaborate
Complex tasks require handoffs between different AI roles
You're building an entire AI team (sales, support, research, etc.)
Tasks need orchestration across many steps and agents
Getting Started
Quick Paths to Building Agents
New to AgenticFlow? Start here:
Agents Quickstart - Build your first agent in 10 minutes
Visual Agent Builder Guide - Complete walkthrough of the visual interface
Ready to customize? Learn the configuration system:
Visual Configuration Overview - How to configure every aspect of your agent
The 11 Agent Capabilities
AgenticFlow agents are built by configuring 11 powerful capability areas. Each capability is independent - use only what you need, or combine them all for maximum power.
1. Identity & Personality
What it controls: Your agent's name, description, and behavior Set up:
Choose a name and description that reflects the agent's purpose
Write a system prompt that defines personality, tone, and expertise
Add a welcome message to greet users
Create suggested conversation starters
Example: A customer support agent might introduce itself as "friendly and professional," use a warm greeting, and suggest starters like "Check my order status" or "I need help with a return."
2. AI Model Selection
What it controls: Which AI model powers your agent and how it thinks
Choose from 100+ models across 7 providers:
OpenAI (17 models): GPT-4.1, GPT-5 family, O1/O3 reasoning models, GPT OSS
Anthropic (8 models): Claude 4.5, 4.0, 3.7, 3.5 families
Google Gemini (5 models): Gemini 3 Pro, 2.5, 2.0, 1.5 families (vision/video/audio)
Groq (14 models): Ultra-fast Llama, Kimi, Qwen, Mistral models
DeepSeek (2 models): Cost-efficient reasoning and chat
PixelML (50+ models): Unified access to all latest models
AgenticFlow (11 models): Budget-friendly curated selection
Fine-tune:
Temperature (0-1): Control creativity vs. consistency (default: 0.1)
Max Tokens: Control response length (1K-128K depending on model)
Context Window: Conversation history (50K-2M tokens depending on model)
Quick Recommendations:
General Business: GPT-4.1 ($2/M), Claude 4.5 Sonnet ($3/M)
High-Volume/Budget: Gemini 2.5 Flash Lite ($0.075/M), GPT-4.1 Nano ($0.08/M)
Coding: GPT-5.1 Codex ($1.25/M), DeepSeek Reasoner ($0.55/M)
Multi-Modal: Gemini 2.5 Flash Lite (vision+video+audio, $0.075/M)
Reasoning: O1/O3 ($1.10/M), DeepSeek Reasoner ($0.55/M)
📚 Complete Model Selection Guide →
3. Knowledge & RAG (Retrieval)
What it controls: Your agent's access to custom knowledge bases, documents, and data Connect to:
Knowledge bases you've created
Datasets and data tables
Uploaded documents (PDFs, Word, text files)
Web pages you've crawled
Configuration options:
Search Strategy: Hybrid search, semantic search, or full-text search
Auto-Retrieval: Automatically search when relevant, or let the agent decide
Top Results (Top-K): How many knowledge chunks to retrieve (1-10)
Relevance Threshold: Minimum similarity score (0-1)
Query Rewrite: Optimize user questions for better retrieval
Reranking: Re-score results for maximum relevance
Example: A product support agent can instantly search through 1,000 product manuals to answer technical questions.
📚 Complete Knowledge Configuration Guide →
4. Workflow Tools (Actions)
What it controls: Workflows your agent can execute as actions during conversations How it works:
Connect existing workflows from your project
Agent automatically calls the workflow when needed
Results are returned to the conversation
Configuration options:
Workflow Selection: Choose which workflow to execute
Tool Description: Define when and how the agent should use this workflow
Timeout: 1-300 seconds per execution (default: 150s)
Input Config: Pre-fill constant values, security constraints, or workspace context
Example: A sales agent can execute a "Create Lead" workflow that adds prospects to your CRM automatically during conversations.
📚 Complete Workflow Tools Configuration Guide →
5. MCP Tools (300+ Integrations)
What it controls: External tools and services your agent can use via the Model Context Protocol Available categories:
CRM systems (Salesforce, HubSpot)
Databases (PostgreSQL, MySQL, MongoDB)
Cloud storage (Google Drive, Dropbox, S3)
Communication (Slack, Email, SMS)
Search engines and APIs
300+ total integrations
Configuration:
Select which tools from each MCP client the agent can use
Set auto-run or request confirmation per tool
Configure timeouts (1-300 seconds)
Override authentication headers
Example: An assistant agent can search Google, check your calendar, send Slack messages, and update spreadsheets - all in one conversation.
📚 Complete MCP Tools Configuration Guide →
6. Plugin Tools (Direct Node Execution)
What it controls: Individual workflow nodes your agent can execute directly How it works:
Select specific nodes from the 193+ available workflow nodes
Agent executes the node with custom inputs
Perfect for simple, single-step actions
Popular plugins:
API calls to any REST endpoint
Data transformation and formatting
File operations
Text processing and extraction
View all 193+ available nodes →
Example: An agent can call a "Send Email" node directly without building an entire workflow.
📚 Complete Plugin Tools Configuration Guide →
7. Sub-Agents (Agent Delegation)
What it controls: Other agents your agent can delegate tasks to How it works:
Create specialized agents for different tasks
Main agent routes requests to the right specialist
Sub-agents complete tasks and report back
Use cases:
Technical support agent → Escalates to billing specialist agent
Intake agent → Routes to sales, support, or account management
General assistant → Delegates research to research specialist
Example: A main receptionist agent delegates appointment scheduling to a calendar agent and customer inquiries to a support agent.
📚 Complete Sub-Agents Configuration Guide →
Learn more → for full multi-agent orchestration
8. Code Execution (Python Sandbox)
What it controls: The agent's ability to write and run Python code Capabilities:
Perform calculations and data analysis
Process files and text
Generate charts and visualizations
Execute custom logic
Security:
Runs in isolated sandbox environment
Optional file system access (restricted to project Drive)
Example: A data analyst agent can load a CSV, perform statistical analysis, and generate a summary report with charts.
📚 Complete Code Execution Configuration Guide →
9. File Attachments (Multi-Modal Input)
What it controls: What types of files users can send to your agent Supported formats:
Images (JPEG, PNG, GIF, WEBP)
Documents (PDF, Word, text)
Audio files
Video files
Configuration:
Max files per message: 1-10 (default: 5)
Max file size: 1-50MB per file (default: 10MB)
Example: A support agent can analyze screenshots of errors, a design agent can critique uploaded images, or a transcription agent can process audio recordings.
📚 Complete Chat Features Configuration Guide →
10. Task Management
What it controls: Whether your agent can create, track, and manage tasks Capabilities:
Create tasks during conversations
Assign tasks to users or teams
Track task status and completion
Link conversations to tasks
Use cases:
Support agents creating follow-up tickets
Sales agents tracking action items
Project assistants organizing work
Example: During a support conversation, the agent creates a task for the engineering team to investigate a bug report.
📚 Complete Task Management Configuration Guide →
11. Structured Output (Schema Validation)
What it controls: Format agent responses as structured JSON data How it works:
Define a JSON schema for the response format
Agent always returns data matching your schema
Perfect for feeding into other systems
Use cases:
Extract structured data from unstructured text
Generate forms and database records
Return consistent API responses
Integrate with business systems
Example: A lead qualification agent always returns responses in this format:
{
"lead_name": "John Smith",
"company": "Acme Corp",
"email": "[email protected]",
"qualified": true,
"interest_level": "high",
"next_steps": "Schedule demo"
}How Agents Work Behind the Scenes
Understanding how agents think and operate helps you build better AI assistants.
Conversation Threads
Every agent interaction happens in a thread - a persistent conversation:
Automatic Creation: Threads are created automatically when a user starts chatting
Title Generation: Threads can auto-generate titles based on the conversation
History Preservation: All messages are saved and available for context
Multiple Conversations: Each user can have multiple separate threads with the same agent
How Agents Think (ReAct Pattern)
Agents use a sophisticated reasoning process:
Understand the user's message and intent
Reason about what information or actions are needed
Act by using tools, searching knowledge, or executing workflows
Observe the results of those actions
Respond with a helpful answer
This cycle can repeat multiple times in a single response. You can control how many steps an agent can take with the recursion limit (10-100 steps, default: 25).
Streaming Responses
Agents stream responses in real-time, just like ChatGPT:
See responses appear word-by-word
Track tool usage as it happens
Cancel long-running responses if needed
Get immediate feedback, not delayed replies
Smart Context Management
Agents automatically manage conversation history:
Context Window: The amount of conversation history the agent can "see"
Automatic Trimming: Old messages are removed when the context gets too large
Token Counting: System tracks exactly how much context is used
Suggested Replies (Optional)
Enable this feature to have the agent suggest follow-up questions or actions after each response. This uses additional credits but creates a more interactive experience.
Agent Templates & Marketplace
Start faster with pre-configured agent templates.
Popular Templates:
Customer Support Agent - Answer questions, look up orders, create tickets
Sales Assistant - Qualify leads, schedule demos, provide product info
Research Assistant - Search knowledge bases, summarize documents, cite sources
Code Helper - Debug code, explain functions, write documentation
Content Creator - Draft emails, write social posts, create marketing copy
Data Analyst - Analyze datasets, create visualizations, generate reports
Personal Assistant - Manage calendar, send reminders, organize tasks
Knowledge Base Q&A - Answer questions from company documentation
Creating Templates
Turn your own agents into reusable templates:
Build and test your agent
Save as template (private, workspace, or public)
Add example conversations to help others understand usage
Share in the marketplace or keep private for your team
Building Great Agents: Best Practices
Learn from what works. Follow these proven patterns for success.
Writing Effective System Prompts
Be Specific About the Role
✅ Good: "You are a customer service agent for Acme Corp's software products.
You help users troubleshoot technical issues, process refunds, and escalate
complex problems to human agents."
❌ Vague: "You are a helpful assistant."Set Clear Boundaries
✅ Good: "You can help with order status, returns, and general product questions.
For billing disputes or account changes, escalate to a human agent.
Never share customer data or make promises about features we don't have."
❌ Unclear: "Help users with whatever they need."Include Examples of Good Interactions Show the agent how you want it to behave with 2-3 example conversations in your system prompt.
Use Personality Consistently Pick a tone (professional, friendly, technical, casual) and maintain it throughout the system prompt.
Choosing the Right Tools
Less is More Only add tools the agent actually needs. Each tool:
Increases response time (agent has to consider it)
Uses more credits (larger prompts)
Adds complexity (more things that can go wrong)
Maximum 50 tools per agent
Use Clear Tool Descriptions The agent decides which tool to use based on descriptions. Make them specific:
✅ Good: "Get customer order status by order ID. Returns shipping status,
tracking number, and estimated delivery."
❌ Vague: "Order lookup tool."Choose Execution Mode Carefully
Auto-Run: For safe, read-only operations (lookup order, search knowledge)
Request Confirmation: For actions that change data (send email, create ticket, charge credit card)
Setting Appropriate Limits
Recursion Limit: How many steps the agent can take
Low (10-15): Simple Q&A agents, minimal tool use
Medium (20-30): Standard agents with moderate complexity (default: 25)
High (40-100): Research agents, complex multi-step tasks
Timeouts: How long tools can run
Short (10-30s): Quick API calls, database queries
Medium (60-120s): File processing, email sending
Long (180-300s): Complex workflows, data analysis
Temperature Settings for Different Use Cases
0.0-0.2: Deterministic, consistent responses (customer support, data extraction)
0.3-0.5: Balanced creativity and consistency (general assistants)
0.6-0.8: More creative responses (content creation, brainstorming)
0.9-1.0: Maximum creativity (creative writing, idea generation)
Deploying Your Agent
Once you've built and tested your agent, make it available to users.
Sharing Options
1. Web Chat Widget Embed your agent directly into your website. Users chat without leaving your site.
2. Public Chat URL Share a direct link to your agent. Anyone with the link can start a conversation (if agent is public).
3. Messaging Platform Integration Publish your agent to:
Discord - Automated server support and community engagement
Slack - Internal knowledge assistant and workflow automation
Telegram - Customer support and notifications
WhatsApp - Direct customer communication
Cost: 100 credits per platform publish
4. Webhooks & Triggers Trigger agent conversations from external events:
Form submissions on your website
Incoming emails
Scheduled tasks
Third-party app events
Testing Before Launch
The Testing Checklist:
Basic Conversation - Start a chat, ensure agent responds
Conversation Starters - Test every suggested prompt
Knowledge Retrieval - Ask questions that require searching your knowledge base
Tool Execution - Trigger each tool at least once
Error Handling - Try to break it (invalid inputs, nonsense questions)
Edge Cases - Test boundary conditions (very long messages, multiple file attachments)
Multi-Turn Conversations - Have a 10+ message conversation to test context
Privacy - Verify the agent doesn't share sensitive information
Monitoring & Optimization
Track performance and continuously improve your agent.
Key Metrics to Monitor
Usage Metrics:
Conversations per day/week
Messages per conversation
Active users
Peak usage times
Quality Metrics:
Average response time
Tool success rate
Knowledge retrieval accuracy
User satisfaction (if you collect feedback)
Cost Metrics:
Credits consumed per conversation
Cost per message
Most expensive tools/models
Token usage trends
Optimization Strategies
If responses are too slow:
Reduce number of tools
Lower recursion limit
Switch to a faster model (GPT-4o Mini, Claude Sonnet)
Reduce knowledge base size
Disable query rewrite and reranking
If costs are too high:
Use smaller models for simple tasks (GPT-4o Mini instead of GPT-4)
Reduce max_tokens limit
Disable suggested replies
Use cheaper models for knowledge retrieval
Optimize system prompt length
If knowledge retrieval is poor:
Enable query rewrite
Adjust top-k (try 5-7 instead of 3)
Lower relevance threshold (try 0.5 instead of 0.7)
Enable reranking
Restructure knowledge base documents
Try hybrid search instead of semantic-only: reranking is automatically use when hybrid search is on.
If tool usage is unreliable:
Improve tool descriptions
Reduce total number of tools
Add examples to system prompt
Use request confirmation for critical tools
Increase tool timeouts
Related Documentation
Core Platform Features
Workflows - Sequential automation and batch processing
Workforce - Multi-agent orchestration and team collaboration
Knowledge Bases - Managing documents and data
Integrations - 300+ available tools via MCP
Enterprise & Deployment
Enterprise Features - Security, compliance, and workspaces
Developer API - API integration and webhooks (for developers)
Learning Resources
AgenticFlow 101 Course - Foundation course for all features
Video Tutorials - Visual walkthroughs
Use Cases by Industry - Real-world examples and templates
Support
Troubleshooting Guide - Comprehensive problem-solving
Community & Support - Ask questions and get help
Quick Reference
Essential Settings at a Glance
Model
AI model selection
GPT-4o Mini or Claude 3.5 Sonnet
Temperature
Creativity vs. consistency
0.1 (consistent) to 0.7 (creative)
Recursion Limit
Max reasoning steps
25 steps
Tool Timeout
Max tool execution time
150 seconds
Top-K (Knowledge)
Knowledge chunks to retrieve
5 results
Search Strategy
Knowledge search method
Hybrid search
Max Tokens
Response length limit
Model default
Execution Mode
Tool auto-run behavior
Auto-run (safe tools), Request confirmation (data-changing tools)
Common Agent Types & Settings
Customer Support Agent:
Model: GPT-4o Mini (fast, cost-efficient)
Temperature: 0.2 (consistent, professional)
Knowledge: Product docs, FAQs, policies
Tools: CRM lookup, ticket creation
Recursion limit: 20
Sales Assistant:
Model: Claude 3.5 Sonnet (conversational)
Temperature: 0.4 (balanced)
Knowledge: Product catalog, pricing, case studies
Tools: CRM, calendar, email
Recursion limit: 30
Research Assistant:
Model: GPT-4 (highest quality reasoning)
Temperature: 0.3 (accurate)
Knowledge: Large knowledge bases, documents
Tools: Web search, scholar search
Recursion limit: 50
Data Analyst:
Model: GPT-4o (data handling)
Temperature: 0.1 (precise)
Knowledge: Data dictionaries, analysis guides
Tools: Code execution, file system
Recursion limit: 40
Content Creator:
Model: Claude 3 Opus (creative writing)
Temperature: 0.7 (creative)
Knowledge: Brand guidelines, style guides
Tools: None (pure generation)
Recursion limit: 15
Next Steps
Just Starting?
Try a Template - Start with a pre-built agent
Ready to Build?
Configure System Prompt - Define personality and behavior
Select AI Model - Choose the right model
Connect Knowledge - Add your documents and data
Add Workflow Tools - Connect workflows as actions
Add MCP Tools - Connect to external systems
Enable Drive Access - Access file storage
Set Up Triggers - Configure how to invoke your agent
Configure Sharing - Make it available to users
Need Help?
Troubleshooting Guide - Solve common issues
Community Support - Ask questions
Video Tutorials - Watch how it's done
Ready to build your first AI agent? Start the Quickstart →
Last updated
Was this helpful?