# Iterate and refine your Agent Building an effective AI Agent is an iterative process. Even well-designed agents need refinement based on real-world usage. This section guides you through systematic testing and improvement.
## Testing your Agent Once your Agent is created, you can start testing it in Editor mode as users would. Use this space to validate behavior before publishing. ### **Testing methodology** #### **1. Start with core functionality** * Test your primary use case first. * Verify the Agent uses the correct data sources and tools. * Ensure basic queries return expected results. #### **2. Expand to edge cases** * Try ambiguous queries: "Show me the best locations" * Test boundary conditions: "What about areas with no data?" * Use incorrect inputs: "Analyze store #99999" * Mix multiple requests: "Compare regions and also forecast next quarter" #### **3. Validate different user personas** * Technical user: "Execute a spatial join between stores and demographics" * Business user: "Where should we expand?" * Executive: "Give me the most profitable regions for California" #### **What to observe** During testing, monitor these key behaviors: **Tool execution** * Is the Agent selecting the most efficient tools? * Are queries running successfully within timeout limits? * Do visualizations appear correctly on the map? **Response quality** * Are insights accurate and actionable? * Does the formatting match your intended audience? * Is the Agent avoiding technical jargon when instructed? **Error handling** * How does the Agent respond to unavailable data? * Are error messages helpful or confusing? * Does it offer alternatives when it can't complete a request? ## Common refinement patterns Based on testing, you'll typically need to refine these areas: ### **Refining Use Case and context** **Problem:** Agent misinterprets user intent ```markdown # Before Analyze store performance # After Analyze store performance by comparing actual revenue against target revenue. Default to current quarter unless specified. Flag stores below 80% of target. ``` ### **Data definitions** **Problem:** Agent uses wrong fields or calculations ```markdown # Before Use revenue data for analysis # After $stores.gross_revenue: Total revenue including tax (use for reports) $stores.net_revenue: Revenue after tax (use for profitability) $stores.target_revenue: Monthly targets set by regional managers ``` ### **Tool preferences** **Problem:** Agent creates new queries when widgets exist ```markdown # Before Provide revenue insights # After For revenue summaries, first check the 'Revenue KPI' widget. Only generate new queries for time periods not covered by existing widgets. ``` ### **Response formatting** **Problem:** Responses too technical for intended audience ```markdown # Before Provide analysis results # After Format responses for non-technical executives: - Lead with business impact (dollars or percentage) - Use simple terms (not "spatial join" but "combine location data") - Include one clear recommendation ``` ### Performance optimization If your Agent is slow or hitting timeout limits: * Break multi-step analyses into separate tasks. * Use asynchronous Workflows as MCP tools for heavy computations. * Pre-aggregate data in your map sources where possible. * Refine query patterns ensuring filtering is applied to avoid full table computations. ### Maintenance considerations Your Agent requires ongoing maintenance: * **Data schema changes**: Update field references in Instructions when source schemas change. * **New tool availability**: Incorporate new MCP Tools as they become available. * **Usage pattern evolution**: Refine based on actual user queries from production usage. * **Model updates**: Re-test when switching to newer model versions.