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:

  1. Click Add Data Source

  2. Select from connected sources

  3. Choose specific tags/categories (optional)

  4. 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:

Setting
Description
Token Limit
Best For

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:

Model
Speed
Quality
Cost
Best For

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:

Setting
Description
When to Use

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

  1. Open agent in Playground

  2. Test various query types:

    • Simple factual questions

    • Complex multi-part queries

    • Follow-up questions

    • Edge cases

  3. Review responses and citations

  4. Adjust configuration based on results

A/B Testing

Configuration Validation

Troubleshooting

Agent Not Responding Well

Check:

  1. Are instructions clear and specific?

  2. Are correct data sources connected?

  3. Is RAG strategy appropriate?

  4. 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

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