Agent Configuration
Configure your AI agents for optimal performance with detailed settings for behavior, data access, and deployment.
Overview
Agent configuration determines:
How the agent responds to queries
Which data sources it can access
What tone and style it uses
Performance characteristics
Security and privacy settings
Configuration Sections
1. Basic Information
Required Settings:
Agent Name
Clear, descriptive name
Examples: "Customer Support Agent", "API Documentation Assistant"
Used in UI and API references
Visible to users
Description
Brief explanation of agent's purpose
Helps team members understand use case
Not shown to end users
Example: "Handles billing and subscription questions for support team"
Avatar/Icon
Visual identifier for agent
Upload custom image or generate AI avatar
Recommended: 512x512 pixels, PNG format
Falls back to default if not provided
Status
Active: Agent is live and usable
Inactive: Agent is hidden and unavailable
Draft: Work in progress, testing only
2. Data Source Selection
Control what knowledge the agent can access.
Adding Data Sources:
Click Add Data Source
Select from connected sources
Choose specific tags/categories (optional)
Set priority (for Cypress strategy)
Data Source Configuration:
Best Practices:
Start with 1-3 focused data sources
Add more based on performance
Use tags to filter content
Organize by tier for Cypress strategy
3. Instructions & Prompts
Define how the agent behaves and responds.
System Instructions
The core prompt that defines agent behavior:
Instruction Components:
Identity:
Responsibilities:
Guidelines:
Tone & Style:
Example Instructions by Use Case:
Technical Documentation Agent:
Sales Enablement Agent:
4. Response Configuration
Fine-tune how the agent generates responses.
Response Length:
Concise
Brief, direct answers
150
Quick facts, simple queries
Standard
Balanced explanations
350
Most use cases
Detailed
Comprehensive coverage
600
Complex topics, tutorials
Custom
User-defined
50-1000
Specific needs
Response Style:
Paragraph Format:
Bullet Points:
Step-by-Step:
Code-Focused:
5. Model Configuration
Choose and tune the language model.
Model Selection:
GPT-4o
Fast
Excellent
High
Production, high quality
GPT-4
Medium
Excellent
High
Complex reasoning
GPT-3.5-turbo
Very Fast
Good
Low
High volume, simple queries
Claude 3
Fast
Excellent
High
Long context, analysis
Advanced Parameters:
Temperature (0.0 - 1.0)
0.0: Deterministic, consistent, factual
0.3: Slightly varied, mostly consistent
0.7: Balanced (default)
1.0: Creative, varied, unpredictable
Max Tokens (50 - 4000)
Sets maximum response length
Includes response + context
Lower = faster, cheaper
Higher = more comprehensive
Top P (0.0 - 1.0)
Nucleus sampling parameter
0.9 (default) works for most cases
Lower = more focused
Higher = more diverse
Frequency Penalty (0.0 - 2.0)
Reduces word repetition
0.0 = no penalty
0.5 = moderate penalty (recommended)
2.0 = strong penalty
Presence Penalty (0.0 - 2.0)
Encourages topic diversity
0.0 = no penalty
0.5 = moderate penalty
2.0 = strong penalty
6. RAG Strategy
Choose retrieval and generation approach.
Strategy Options:
Redwood (Standard RAG)
Cedar (Context-Aware)
Cypress (Advanced)
See RAG Strategies Overview for detailed comparison.
Strategy Configuration:
7. Privacy & Security
Control data access and security.
Privacy Mode:
Private Data Only
Uses only organization-specific data sources
No external knowledge
Higher security
More controlled responses
Recommended for: Sensitive information, compliance
Public + Private Data
Accesses org data + general knowledge
Broader coverage
More comprehensive answers
Recommended for: General use cases
Data Access Control:
Security Settings:
PII Detection:
Automatically detect personally identifiable information
Mask or redact sensitive data
Log PII access attempts
Content Filtering:
Block inappropriate content
Filter specific keywords
Enforce content policies
Rate Limiting:
8. Citation & Source Settings
Configure how sources are cited.
Citation Behavior:
Always Cite
Every response includes sources
High trust requirements
Cite When Used
Only when directly quoting
Balanced approach (default)
Minimal Citation
Only for factual claims
Conversational agents
No Citations
Never show sources
Not recommended
Source Display:
9. Conversation & Memory
Manage conversation context.
Memory Configuration:
Session-Based Memory:
User-Level Memory:
Memory Behavior:
Ephemeral: Reset after session
Short-term: Remember for 24 hours
Long-term: Persist indefinitely
User-controlled: User can clear
10. Advanced Features
Optional advanced configurations.
Agentic Workflows:
Enable tool use and function calling:
Custom Metadata:
Webhook Integration:
Configuration Templates
Customer Support Agent
Technical Documentation Agent
Sales Enablement Agent
Best Practices
1. Start Simple
✅ Begin with basic configuration ✅ Add 1-2 focused data sources ✅ Use default RAG strategy (Cedar) ✅ Test thoroughly before advancing ❌ Don't overconfigure initially
2. Clear Instructions
✅ Be specific about behavior ✅ Include examples ✅ Define boundaries ✅ Specify tone and style ❌ Don't write vague instructions
3. Appropriate Strategy
✅ Redwood: Simple, high-volume ✅ Cedar: Conversational, general use ✅ Cypress: High accuracy, complex ❌ Don't use Cypress for everything
4. Regular Review
✅ Review configuration monthly ✅ Update based on analytics ✅ Adjust based on feedback ✅ Test after changes ❌ Don't "set and forget"
Testing Configuration
Playground Testing
Open agent in Playground
Test various query types:
Simple factual questions
Complex multi-part queries
Follow-up questions
Edge cases
Review responses and citations
Adjust configuration based on results
A/B Testing
Configuration Validation
Troubleshooting
Agent Not Responding Well
Check:
Are instructions clear and specific?
Are correct data sources connected?
Is RAG strategy appropriate?
Is temperature too high/low?
Inconsistent Responses
Solutions:
Lower temperature (0.3-0.5)
More specific instructions
Add examples in system prompt
Switch to Cedar for context
Slow Performance
Solutions:
Switch to Redwood strategy
Reduce topK
Use GPT-3.5-turbo
Reduce max tokens
Next Steps
Prompt Engineering - Craft effective prompts
RAG Strategies - Choose optimal strategy
Agent Deployment - Deploy your agent
Performance Tuning - Optimize performance
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