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Creating an Agent

Click Agents in the sidebar → Create Agent. Agent Name: Descriptive name like “Company Enrichment Agent” or “Resume Parser”. Avoid generic names. Description: Explain what the agent does, what data it processes, and expected outputs.

Configure AI Model

GPT-4: Best for complex reasoning and detailed analysis. Higher cost, slower. GPT-4 Mini: Balanced performance and cost. Good for standard data processing. GPT-4 Nano: Fast, low cost, simple tasks only. API Key: Select from existing variables or create new. Never paste keys directly—always use secure variables.

Write System Prompt

The system prompt defines your agent’s role, instructions, output format, and guidelines.

Structure

Role: Define who the agent is
Task: Explain what to do
Output Format: Specify exact format
Guidelines: Add important rules

Select Tools (Optional)

Choose tools that extend agent capabilities: web search, company databases, social media APIs, document processing, custom APIs.
Start with fewer tools and add as needed. Too many tools slow down agents and increase costs.

Test Your Agent

Click Test Agent → Provide sample input → Review response → Check formatting, accuracy, completeness. Test with typical cases, edge cases, missing information, and ambiguous data.

Save and Deploy

Review configuration → Create Agent → Agent is now active.

Connect to Dataset

Navigate to dataset → Click column header → Edit Column → Select your agent in Agent dropdown → Add column-specific instructions if needed → Save.

Next Steps

Test Agents

Test and validate agents

Chat with Agents

Interactive agent testing

Manage Agent Tools

Add and configure tools

Configure Models

Advanced model configuration