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Best Practices

Follow these guidelines to build effective, efficient agents that deliver accurate insights while managing costs.

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Model Selection

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Choosing the Right Model

Different models excel at different tasks:
Model TypeBest ForCostSpeed
GPT-4Complex reasoning, detailed analysisHighSlower
GPT-4oBalanced performance, general useMediumMedium
GPT-4o-miniSimple queries, high volumeLowFast
Claude 3.5 SonnetCode generation, structured dataMediumMedium
Claude 3 HaikuQuick answers, basic queriesLowFast

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Use Case Guidelines

Complex Analysis → GPT-4 or Claude 3.5 Sonnet
  • Multi-step reasoning required
  • Deep data analysis
  • Code generation needs
General Use → GPT-4o or Claude 3.5 Sonnet
  • Balanced performance
  • Most common questions
  • Good for production agents
High Volume → GPT-4o-mini or Claude 3 Haiku
  • Simple queries
  • Cost-sensitive applications
  • Fast response times needed
Start with a mid-tier model (GPT-4o or Claude 3.5 Sonnet) and adjust based on actual usage patterns.

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Model Parameters

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Temperature

Temperature controls how creative or focused the agent’s responses are:
Temperature Scale
  • 0.0 - 0.3 (Focused)
    • Consistent, deterministic answers
    • Best for: Data queries, factual analysis
    • Use when accuracy is critical
  • 0.4 - 0.7 (Balanced)
    • Mix of consistency and creativity
    • Best for: General purpose agents
    • Recommended default
  • 0.8 - 1.0 (Creative)
    • Varied, exploratory responses
    • Best for: Brainstorming, recommendations
    • Use sparingly for data agents
For most data analysis agents, keep temperature between 0.2 and 0.5 for reliable results.

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Max Tokens

Max Tokens controls the maximum response length:
  • 1,000 - 2,000 - Short, focused answers
  • 2,000 - 4,000 - Standard responses (recommended)
  • 4,000+ - Detailed analysis and long explanations
Higher limits = more cost. Set based on expected answer complexity.

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Data Source Optimization

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Use Views Instead of Datasets

Why Views Are Better:
  • ✅ Faster query execution
  • ✅ Pre-filtered, relevant data only
  • ✅ Better security (control data access)
  • ✅ Lower token usage
  • ✅ More accurate agent responses
Example: 
Instead of: Full "orders" dataset (millions of rows)
Create: "recent_orders" view (last 90 days, key columns)
The agent queries faster and gets relevant data immediately.
Always use Views instead of raw datasets for better performance and cost efficiency.

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Limit Data Sources

Only grant access to necessary datasets/views: Too Broad:
  • Sales data
  • HR data
  • Marketing data
  • Finance data
Focused:
  • Sales summary view
  • Revenue trends view
Fewer sources = faster decisions + lower costs.

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Optimize View Definitions

Create views with:
  • Only necessary columns
  • Pre-aggregated data where possible
  • Relevant date ranges
  • Indexed fields

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Agent Instructions

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Write Clear System Prompts

Good Instructions: 
You are a sales analytics expert. Help users analyze:
- Revenue trends and forecasting
- Customer behavior and segmentation
- Product performance metrics

Always provide specific numbers and dates. When showing
trends, create visualizations. Be concise and actionable.
Poor Instructions: 
You are helpful. Answer questions about data.

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Define Scope

Tell the agent:
  • What data it has access to
  • What questions it should handle
  • What format responses should take
  • Any business rules to follow

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Include Examples

Provide specific examples in instructions: 
When asked about "top customers," show:
1. Customer name
2. Total revenue
3. Number of orders
4. Comparison to average

Example format:
"Top 5 customers by revenue:
1. Acme Corp - $150K (45 orders) - 3x average
..."

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Cost Optimization

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Control Token Usage

Input Tokens:
  • Keep system instructions concise
  • Limit conversation history length
  • Use focused data sources
Output Tokens:
  • Set appropriate max_tokens
  • Request concise answers when possible
  • Avoid asking for repeated information

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Monitor Spending

  • Review token usage per conversation
  • Track costs by agent
  • Set up alerts for high usage
  • Regularly audit agent performance

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Batch Similar Questions

If running automated analysis:
  • Group related queries
  • Reuse context where possible
  • Cache frequently accessed data

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Agent Configuration

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Tool Selection

Enable only what you need: Optional Tools:
  • Chart Generator - For data visualizations (recommended for most agents)
  • Python Executor - For complex calculations and advanced analytics
  • Web Search - For external context and current information
Query Executor is enabled by default and cannot be disabled.

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Data Access Control

  • Grant minimum necessary access
  • Use views to restrict data
  • Separate agents by use case
  • Review permissions regularly

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Regular Maintenance

  • Update instructions based on user feedback
  • Refine data sources as needs change
  • Adjust model selection for cost/performance
  • Archive unused agents

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Query Performance

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Faster Queries

  1. Use Views - Pre-filtered, optimized data
  2. Limit Result Sets - Ask for “top 10” not “all”
  3. Specific Time Ranges - “last month” not “all time”
  4. Indexed Columns - Ensure views use indexed fields

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Example Optimizations

Slow: “Show all customer transactions” Fast: “Show top 20 customers by revenue this quarter”

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Testing & Validation

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Test Common Questions

Before deploying:
  • Ask typical user questions
  • Verify data accuracy
  • Check tool usage patterns
  • Measure response times

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Validate Answers

  • Compare agent responses to known results
  • Verify calculations manually
  • Check chart accuracy
  • Test edge cases

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Iterate

  • Gather user feedback
  • Refine instructions
  • Adjust parameters
  • Optimize data sources

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Security

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Data Access

  • Only grant necessary permissions
  • Use views to limit sensitive data
  • Review agent access regularly
  • Audit query logs

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Credentials

  • Secure model account API keys
  • Rotate credentials periodically
  • Monitor for unusual usage
  • Implement access controls

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Next Steps