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Creating Your First Agent

1

Navigate to Custom Agents

Go to julius.ai/custom-agents or click Custom Agents in your sidebar menu, then click Create Agent.
2

Name Your Agent

Give your agent a descriptive name that reflects its purpose (e.g., “Marketing Analytics Expert” or “Financial Reporting Assistant”).
3

Configure Your Agent

Fill in the configuration fields to customize how your agent behaves and what it knows.

How Configuration Components Work Together

When you configure a custom agent, each component plays a specific role in shaping how the agent responds to your questions: How Custom Agents Work
  • Training Tasks provide specific examples that teach your agent how to approach similar questions
  • Instructions define the general methodology and behavior guidelines applied to every interaction
  • Knowledge Base contains contextual information that’s automatically summarized and injected when relevant to your question
  • System Prompt combines all these elements with your chat history to generate responses
This architecture ensures your agent has the right context, methodology, and examples to provide consistent, high-quality responses.

Configuration Options

Agent Configuration

Agent Name & Description

Agent Name: Choose a clear, descriptive name for your agent. Description: Write a brief description that explains what your agent does. This helps you and your team quickly identify the right agent for the task.

Behavior Settings

Control how your agent responds with the behavior slider:
  • Short: Concise, to-the-point responses
  • Standard (Default): Balanced responses with appropriate detail
  • Detailed: Comprehensive responses with thorough explanations

Output Format

Choose what type of output your agent prioritizes:
  • All (default): Balanced approach to all output types
  • Visualizations First: Prioritizes creating charts and graphs
  • Files/DataFrames First: Focuses on data tables and exports
  • Insights First: Emphasizes written analysis and takeaways

Instructions

Character Limit: 1,000 characters Tell your agent how to approach analysis in general. This is where you define the agent’s methodology and high-level guidelines. Example:
Always start by asking clarifying questions about the data. 
Focus on actionable insights for marketing campaigns. 
Highlight trends and anomalies in customer behavior. 
Create visualizations before providing detailed explanations.
Keep instructions focused on how to work rather than specific tasks. Save specific examples for Training Tasks.

Training Tasks & Approach

Training Tasks Train your agent on specific tasks with concrete examples. You can add up to 5 training tasks. Each task has two parts:
  • Task: The question or request (e.g., “How many active subscriptions do we have?”)
  • How to approach: Step-by-step instructions for completing this task
Example: Task: “Show me monthly revenue trends” How to approach:
Query the transactions table, group by month using DATE_TRUNC.
Calculate total revenue per month.
Create a line chart showing the trend.
Highlight any months with >10% change from previous month.
Provide a brief summary of the overall trend.
Why Use Training Tasks?
  • Ensures consistency on repeated tasks
  • Teaches best practices to your agent
  • Reduces the need for detailed instructions each time
  • Great for onboarding team members to your analytical standards
Training tasks provide examples, but your agent can still handle variations and new questions. These are learning examples, not strict rules.

Knowledge Base

Character Limit: 10,000 characters Provide context about your company, data, or domain that the agent should know. This information is automatically summarized and applied when relevant to your questions. What to Include:
  • Company background and business model
  • Data structure and key tables/fields
  • Industry-specific terminology
  • Important metrics and how they’re calculated
  • Common data issues or quirks
  • Relationships between different data sources
Example:
Our company is a B2B SaaS platform selling project management software.

Pricing Tiers:
- Starter: $49/month (up to 10 users)
- Professional: $149/month (up to 50 users)  
- Enterprise: Custom pricing (unlimited users)

Key Metrics:
- MRR (Monthly Recurring Revenue): Sum of all active subscription values
- Churn Rate: Cancelled subscriptions / total active subscriptions
- CAC (Customer Acquisition Cost): Marketing spend / new customers

Database Structure:
- users table: Contains customer account information
- subscriptions table: Active and cancelled subscription data
- transactions table: Payment history and invoices
- usage_logs table: Product usage and feature adoption data

Important Notes:
- Free trial users have subscription_status = 'trial'
- Enterprise deals are marked as custom_pricing = true
- Usage data is logged daily at midnight UTC
The knowledge base is intelligently summarized for each query. Julius automatically extracts only the relevant information needed to answer your specific question.
Toggle whether your agent can use web search to find current information beyond its training data. Useful for agents that need to reference current events, prices, or frequently changing information.

Saving Your Agent

Once you’ve configured your agent:
  1. Click Save to store your configuration
  2. Click Use Agent to activate it immediately and start a new chat
  3. Or click Share with Team to make it available to team members
You can edit and refine your agent at any time. As you learn what works best, update the configuration to improve performance.

Best Practices

Start Simple

Begin with basic instructions and a few training tasks. Add complexity as you understand what works.

Be Specific

Vague instructions lead to inconsistent results. Clear, specific guidance helps your agent deliver exactly what you need.

Use Real Examples

Base training tasks on actual questions you regularly ask. This ensures your agent handles real-world scenarios.

Iterate and Improve

Monitor your agent’s performance and refine the configuration. Add new training tasks as you discover common patterns.

Document Your Data

A well-maintained knowledge base makes a huge difference. Keep it updated as your data structure changes.

Next Steps