Overview

AI Agents are the core of the Twig AI platform. They are intelligent assistants that can answer questions, provide information, and assist users based on your organization's knowledge base.

What is an AI Agent?

An AI Agent is a configured instance of the RAG (Retrieval-Augmented Generation) system that combines:

• Data Sources: The knowledge bases the agent can access

• Instructions: Custom prompts and behavior guidelines

• RAG Strategy: Redwood, Cedar, or Cypress

• Model Configuration: Which language model to use and its parameters

• Deployment Settings: Where and how the agent is accessible

Think of an agent as a specialized AI assistant tailored for a specific use case, audience, or knowledge domain.

Agent Components

1. Configuration

Basic Settings:

• Name and description

• Avatar/icon

• Status (active/inactive)

Behavior Settings:

• System instructions

• Response style (concise, detailed, technical)

• Tone (professional, friendly, casual)

• Language preferences

2. Data Sources

Agents can access one or more data sources:

• Connected during agent creation

• Can be added/removed anytime

• Restricted by permissions and tiers

• Optional: limit to specific tags or categories

3. RAG Strategy

Choose the retrieval strategy based on your needs:

Strategy	Speed	Quality	Best For
Redwood	Fast (~1-2s)	Good	Clear, simple questions
Cedar	Medium (~2-3s)	Better	Conversational queries
Cypress	Slower (~3-4s)	Best	Complex, high-stakes questions

See RAG Strategies for detailed comparison.

4. Model Selection

Choose the language model:

• GPT-4o: Best quality, most capable

• GPT-4: High quality, good reasoning

• GPT-3.5-turbo: Fast, cost-effective

• Custom models: Bring your own (Enterprise)

5. Workflow Type

Standard Workflow:

• Direct question → answer

• Fastest response

• Best for most use cases

Agentic Workflow:

• Can use tools and functions

• Multi-step reasoning

• Can break down complex queries

• Best for complex problem-solving

Agent Types

Customer Support Agent

• Access to help documentation, FAQs, product guides

• Friendly, helpful tone

• Fast responses (Redwood strategy)

• Focus on resolution

Technical Documentation Agent

• Access to API docs, technical specs, code examples

• Technical, precise tone

• High accuracy (Cypress strategy)

• Detailed explanations

Sales Enablement Agent

• Access to product info, pricing, competitive intel

• Professional, persuasive tone

• Balanced speed and quality (Cedar strategy)

• Feature-focused

Internal Knowledge Agent

• Access to all company documentation

• Professional tone

• Private data only

• Comprehensive access

Creating an Agent

See Creating Your First Agent for a step-by-step guide.

Quick steps:

1. Navigate to Agents → Create New Agent

2. Configure basic settings (name, description)

3. Select data sources

4. Choose RAG strategy

5. Add custom instructions

6. Test in Playground

7. Deploy

Agent Lifecycle

┌─────────┐
│ Create  │
└────┬────┘
     │
     ▼
┌─────────┐
│Configure│
└────┬────┘
     │
     ▼
┌─────────┐
│  Test   │ ← Iterate
└────┬────┘
     │
     ▼
┌─────────┐
│ Deploy  │
└────┬────┘
     │
     ▼
┌─────────┐
│ Monitor │ → Review Inbox, Analytics
└────┬────┘
     │
     ▼
┌─────────┐
│ Improve │ → Edit instructions, add data sources
└────┬────┘
     │
     └─────→ (back to Test)

Agent Settings

Privacy Settings

Private Data Only:

• Uses only organization-specific data sources

• No external information

• Higher security

• More controlled responses

Public + Private Data:

• Can access both org data and public information

• Broader knowledge

• More comprehensive answers

Response Configuration

Response Length:

• Concise: 1-2 paragraphs

• Standard: 2-4 paragraphs

• Detailed: Comprehensive explanations

Citation Behavior:

• Always cite sources

• Cite only when necessary

• No citations (not recommended)

Confidence Threshold:

• Only answer when confident

• Attempt all questions

• Admit uncertainty when appropriate

Advanced Settings

Temperature: Control creativity (0.0 - 1.0) Max Tokens: Limit response length Top P: Nucleus sampling parameter Frequency Penalty: Reduce repetition Presence Penalty: Encourage topic diversity

Managing Agents

Viewing Agents

The Agent Hub displays all your agents:

• Agent list with key metrics

• Search and filter

• Sort by name, creation date, or usage

• Quick actions (edit, duplicate, delete)

Editing Agents

1. Click on an agent to open settings

2. Modify any configuration

3. Save changes

4. Test in Playground to verify

Changes take effect immediately.

Duplicating Agents

Create variations of existing agents:

1. Open the agent you want to duplicate

2. Click Duplicate

3. Modify the copy as needed

4. Save with a new name

Use cases:

• A/B testing different configurations

• Creating specialized versions

• Staging vs production agents

Archiving Agents

Deactivate agents without deleting:

• Preserves configuration and history

• Can be reactivated later

• Doesn't count toward agent limits

Deleting Agents

⚠️ Warning: Deletion is permanent and will:

• Remove agent configuration

• Invalidate API integrations

• Remove from all deployments

• Preserve interaction history (for analytics)

Best Practices

Start Simple

• Begin with a focused knowledge base

• Add data sources incrementally

• Test thoroughly before adding more

Clear Instructions

• Be specific about behavior expectations

• Include examples of good responses

• Define boundaries (what not to answer)

Choose the Right Strategy

• Redwood: When speed matters

• Cedar: For conversational use cases

• Cypress: When accuracy is critical

Monitor and Iterate

• Review interactions in Inbox

• Track quality metrics in Analytics

• Continuously improve based on feedback

Test Before Deploying

• Use Playground extensively

• Test edge cases

• Involve stakeholders in testing

• Document test scenarios

Next Steps

• Creating Your First Agent

• RAG Strategies Deep Dive

• Prompt Engineering

• Agentic Workflows

• Agent Deployment