Agent Types - Evo AI

Overview

The Evo AI platform offers different types of agents, each optimized for specific use scenarios. From simple LLM-based agents to complex workflows with multiple steps, you can choose the approach that best adapts to your needs.

Smart Choice: Each agent type has been designed to solve specific problems. Use this guide to identify which type best serves your use case.

Available Types

Fundamental Agents

LLM Agent

Base agent with LLMFundamental agent based on Large Language Models, ideal for conversations, text analysis, and tasks requiring natural language understanding.When to use:

Intelligent conversations
Text analysis
Content generation
NLP tasks

Features:

Chat interface
Multiple LLM models
Personality configuration
Sub-agent system

A2A Agent

Integration with A2A protocolIntegrates external agents that implement Google’s Agent-to-Agent (A2A) protocol, enabling interoperability with external systems.When to use:

Integration with legacy systems
External specialized agents
AI microservices
Third-party APIs

Features:

JSON-RPC 2.0 protocol
Standardized communication
Automatic health checks
Circuit breakers

Orchestration Agents

Sequential Agent

Ordered sequential executionExecutes multiple sub-agents in ordered sequence, where each agent receives the output of the previous one as input.When to use:

Processing pipelines
Linear workflows
Dependencies between steps
Data transformation

Features:

Step-by-step execution
Data passing between agents
Failure control
Detailed logs

Parallel Agent

Simultaneous parallel executionExecutes multiple sub-agents simultaneously, ideal for independent tasks that can be processed in parallel.When to use:

Independent analyses
Distributed processing
Time optimization
Unrelated tasks

Features:

Simultaneous execution
Result aggregation
Concurrency control
Significant speedup

Loop Agent

Iterative execution with refinementExecutes sub-agents in iterative loops, refining results with each iteration until reaching convergence criteria.When to use:

Iterative refinement
Parameter optimization
Progressive improvement
Result convergence

Features:

Controlled iterations
Stop criteria
Convergence monitoring
Infinite loop prevention

Workflow Agent

Complex visual workflowsCreates complex workflows with visual interface using LangGraph and React Flow, offering maximum control and flexibility.When to use:

Complex business logic
Conditions and branching
Visual workflows
Granular control

Features:

Drag-and-drop interface
Multiple node types
Conditions and delays
Integrated testing

Specialized Agents

Task Agent

Specific and structured tasksInspired by CrewAI, allows assigning specific tasks to agents with structured prompts and well-defined expected outputs.When to use:

Well-defined tasks
Single responsibility
Structured output
Agent specialization

Features:

One task per agent
Structured prompt
Defined expected output
Automatic validation

Quick Comparison

Type	Complexity	Use Cases	Execution	Configuration
LLM Agent	Low	Chat, text analysis	Single	Simple
A2A Agent	Medium	External integration	Single	Moderate
Sequential	Medium	Linear pipelines	Sequential	Moderate
Parallel	Medium	Independent analyses	Parallel	Moderate
Loop	High	Iterative refinement	Iterative	Complex
Workflow	Very High	Complex logic	Visual/Conditional	Very Complex
Task	Low-Medium	Specific tasks	Single	Simple-Moderate

Selection Guide

By Problem Complexity

Simple Problems

For direct tasks and conversations:

LLM Agent: Chat, text analysis, content generation
Task Agent: Specific tasks with structured output

Characteristics:

Quick setup
Direct execution
Ideal for getting started

Medium Problems

For processes with multiple steps:

Sequential Agent: When steps depend on each other
Parallel Agent: When steps are independent
A2A Agent: When you need to integrate external systems

Characteristics:

Multi-agent orchestration
Basic flow control
Reuse of existing agents

Complex Problems

For sophisticated business logic:

Loop Agent: When you need to refine results iteratively
Workflow Agent: When you need granular control and conditions

Characteristics:

Advanced conditional logic
Sophisticated flow control
Detailed monitoring

By Execution Pattern

Single Execution

Agents that execute once:

LLM Agent: Direct response to a question
A2A Agent: Call to external system
Task Agent: Execution of specific task

Ideal for:

Point queries
Simple analyses
Direct integrations

Sequential Execution

Agents that execute in order:

Sequential Agent: Pipeline with dependencies

Ideal for:

Data transformation
Linear workflows
Step-by-step processing

Parallel Execution

Agents that execute simultaneously:

Parallel Agent: Independent analyses

Ideal for:

Time optimization
Comparative analyses
Distributed processing

Iterative Execution

Agents that execute in loops:

Loop Agent: Progressive refinement

Ideal for:

Parameter optimization
Iterative improvement
Result convergence

Conditional Execution

Agents with complex logic:

Workflow Agent: Flows with conditions and branching

Ideal for:

Complex business logic
Conditional decisions
Visual workflows

Use Case Examples

E-commerce

Product Analysis

Scenario: Complete product analysis for optimizationRecommended solution: Parallel Agent

Price analysis (specialized sub-agent)
Review analysis (sentiment sub-agent)
Specification analysis (technical sub-agent)
Competition analysis (market sub-agent)

Why Parallel? Independent analyses that can be executed simultaneously to optimize time.

Lead Qualification

Scenario: Sales lead qualification pipelineRecommended solution: Sequential Agent

Data enrichment (public data)
Lead scoring (scoring algorithm)
Priority classification (high/medium/low)
Salesperson routing (based on specialization)

Why Sequential? Each step depends on the previous one to function correctly.

Content and Marketing

Content Creation

Scenario: Optimized blog article generationRecommended solution: Loop Agent

Initial content generation
SEO analysis and optimization
Quality and readability review
Refinement based on feedback

Why Loop? Iterative improvement until reaching desired quality.

Performance Analysis

Scenario: Campaign performance reportRecommended solution: Task Agent

Specific task: generate metrics report
Input: campaign data
Output: structured report with insights

Why Task? Well-defined task with specific structured output.

Customer Service

Smart Support

Scenario: Support system with escalationRecommended solution: Workflow Agent

Initial request classification
Automatic resolution attempt
Condition: resolved? → End : Escalation
Routing to specialized human agent

Why Workflow? Complex conditional logic with multiple paths.

Basic Chat

Scenario: Simple chat for frequent questionsRecommended solution: LLM Agent

Direct conversation with customer
Integrated knowledge base
Contextual responses

Why LLM? Direct and simple interaction, no need for orchestration.

New Feature: All agent types now support Output Key for state sharing between agents and processes.

What are Output Keys?

The Output Key is a field available in all agent types that allows saving the execution result in a specific variable in the shared state. This facilitates communication between agents and building complex systems.

LLM Agent

Saves model responseThe response generated by the LLM is saved in the specified variable, allowing other agents to access the generated content.

Output Key: "analysis_response"
→ state.analysis_response

Task Agent

Saves task resultThe structured result of the task is saved in the variable, allowing use in subsequent tasks or workflows.

Output Key: "processed_data"
→ state.processed_data

A2A Agent

Saves external responseThe response from the external system via A2A protocol is saved in the variable, enabling integration with other agents.

Output Key: "external_result"
→ state.external_result

Workflow Agents

Saves workflow resultThe final result of the workflow (Sequential, Parallel, Loop, Workflow) is saved in the specified variable.

Output Key: "workflow_result"
→ state.workflow_result

How to Use

Configuration

In the platform interface:

Configure the Output Key in any agent
Use a descriptive name in snake_case
The result is saved automatically in the state
Other agents access via placeholders {{output_key_name}}

Example:

Agent 1 - Output Key: "initial_analysis"
Agent 2 - Instructions: "Analyze the request: {{user_input}} and base it on the analysis: {{initial_analysis}}"

Data Flow

How data flows between agents:

Initial state:
{
  "user_input": "Analyze this product"
}

After Agent 1 (output_key: "analysis"):
{
  "user_input": "Analyze this product",
  "analysis": "High quality product..."
}

After Agent 2 (output_key: "recommendation"):
{
  "user_input": "Analyze this product",
  "analysis": "High quality product...",
  "recommendation": "I recommend the purchase because..."
}

Best Practices

Tips for using Output Keys effectively:

Clear naming: Use names that describe the content
Snake_case: analysis_result, processed_data
Be specific: sentiment_analysis_reviews instead of analysis
Document format: Explain expected structure in instructions
Avoid conflicts: Don’t use names already existing in state
Consider hierarchy: Use prefixes to group related data

Agent Management

Export and Import

Exporting agents

Backup and sharing functionality:In the agent listing, you can export any agent to a JSON file that contains the complete configuration:What is exported:

✅ Complete configurations of the main agent
✅ Sub-agents included automatically in the same file
✅ All dependencies and nested configurations
✅ Hierarchical structure preserved
✅ Tools and integrations configured

How to export:

In the agent list, locate the desired agent
Click on the actions menu (⋯) of the agent
Select “Export”
JSON file will be downloaded automatically

Example of exported structure:

{
  "name": "copywriter",
  "description": "Creative writer specialized in innovative copywriting.",
  "role": null,
  "goal": null,
  "type": "llm",
  "model": "openai/gpt-4.1-nano",
  "instruction": "You are a senior copywriter with absolute mastery of persuasion techniques",
  "config": {
    "output_key": "new_copy",
    "tools": null,
    "custom_tools": null,
    "mcp_servers": null,
    "custom_mcp_servers": null,
    "agent_tools": null,
    "sub_agents": null,
    "workflow": null
  },
  "id": "7fd77e46-dfcb-40a2-b71f-d2bac56956ac"
}

Importing agents

Restoring complete configurations:How to import:

In the agent list, click “Import Agent”
Select the JSON file exported previously
System validates structure and dependencies
Main agent and sub-agents are created automatically
Configurations applied exactly as exported

Import advantages:

🚀 Instant setup of complex agents
🎯 Exact replication of tested configurations
👥 Sharing between teams
🔄 Backup and restore of configurations
📦 Migration between environments

Automatic validations:

JSON structure verification
Agent type validation
Dependency checking
Available model confirmation
Required permissions verification

Use cases:

✅ Backup of critical agents
✅ Replication in production environment
✅ Template sharing
✅ Migration between organizations
✅ Configuration versioning

Folder System

Organizing agents in folders

Hierarchical organization:The folder system allows organizing your agents hierarchically and collaboratively:Main features:

📁 Visual organization of agents
👥 Sharing with other users
🔐 Permission control (view/edit)
🏷️ Categorization by project, client, or function
🔍 Search within specific folders

How to create folders:

In the agents screen, click “New Folder”
Folder name: Use descriptive name
Description: Summary of folder content
Access settings: Define who can access
Click “Create” to create

Organization examples:

📁 Sales
├── 🤖 lead_qualifier
├── 🤖 product_demo
└── 🤖 closer_specialist

📁 Technical Support  
├── 🤖 troubleshooter
├── 🤖 documentation_helper
└── 🤖 escalation_manager

📁 Marketing
├── 🤖 content_creator
├── 🤖 social_media_manager
└── 🤖 campaign_analyzer

Folder sharing

Collaboration between users:How to share a folder:

Access the folder you want to share
Click the “Share” icon (👥)
Sharing form will open

Configuring permissions:

Enter the email of the Evo AI user
Select access level:
- 👁️ Viewer: Can see and use agents, but not edit
- ✏️ Editor: Can see, use, edit and create new agents in the folder
Add optional message explaining the sharing
Click “Share” to send invitation

Permission levels:Viewer:

✅ View list of agents in folder
✅ Use agents in conversations
✅ View configurations (read-only)
❌ Edit agent configurations
❌ Create new agents
❌ Delete agents
❌ Share folder with others

Editor:

✅ All Viewer permissions
✅ Edit agent configurations
✅ Create new agents in folder
✅ Delete agents (that they created)
✅ Move agents between folders
❌ Share folder with others (owner only)
❌ Delete the folder

Team collaboration

Use cases for sharing:By department:

📁 "Company ABC Sales" Folder
Shared with: sales-team@company.com
Permission: Editor
Use: Entire team can create/edit sales agents

By project:

📁 "Client XYZ Project" Folder  
Shared with: project-manager@company.com
Permission: Viewer
Use: Manager monitors project agents

By specialty:

📁 "Technical AI Agents" Folder
Shared with: ai-team@company.com
Permission: Editor
Use: Technical team develops specialized agents

For clients:

📁 "Final Client Agents" Folder
Shared with: client@clientcompany.com
Permission: Viewer
Use: Client tests developed agents

Collaboration advantages:

🤝 Teamwork facilitated
🔄 Automatic synchronization of updates
📊 Visibility of team work
🎯 Specialization by area/project
📈 Development scalability
🔐 Granular access control

Access management

Managing permissions:As folder owner, you can:

✅ View all users with access
✅ Change permissions of any user
✅ Remove access from specific users
✅ Transfer ownership of folder
✅ Delete folder (removes everyone’s access)

Automatic notifications:

📧 Invitation email when folder is shared
🔔 Notification when agent is modified by another user
📊 Activity report of shared folder

Audit and control:

📝 Activity log of all users
👤 Modification history with responsible party
⏰ Timestamps of all actions
🔍 Complete traceability of changes

Best practices:

Use Viewer permission by default
Grant Editor only when necessary
Document the purpose of folder in name/description
Review permissions periodically
Remove access from inactive users

Tip: Always start with the simplest type that meets your use case. You can evolve to more complex types as your needs grow.

Next Steps

Start with LLM Agent

Ideal for beginners - create your first conversational agent

Explore Workflows

For complex cases - build advanced visual workflows

A2A Protocol

Understand the protocol for integration with external systems

Quick Guide

Set up your first agent in less than 2 minutes

Get Started

​Overview

​Available Types

​Fundamental Agents

LLM Agent

A2A Agent

​Orchestration Agents

Sequential Agent

Parallel Agent

Loop Agent

Workflow Agent

​Specialized Agents

Task Agent

​Quick Comparison

​Selection Guide

​By Problem Complexity

​By Execution Pattern

​Use Case Examples

​E-commerce

​Content and Marketing

​Customer Service

​Output Keys - State Sharing

​What are Output Keys?

LLM Agent

Task Agent

A2A Agent

Workflow Agents

​How to Use

​Agent Management

​Export and Import

​Folder System

​Next Steps

Start with LLM Agent

Explore Workflows

A2A Protocol

Quick Guide

Overview

Available Types

Fundamental Agents

Orchestration Agents

Specialized Agents

Quick Comparison

Selection Guide

By Problem Complexity

By Execution Pattern

Use Case Examples

E-commerce

Content and Marketing

Customer Service

Output Keys - State Sharing

What are Output Keys?

How to Use

Agent Management

Export and Import

Folder System

Next Steps