Testing Agents

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Chat-Based Testing

Navigate to Agents → Select agent → Start Chat. Test with different conversation types:

• Simple queries: Basic understanding and responses
• Complex requests: Multi-step tasks requiring reasoning
• Edge cases: Unusual inputs or boundary conditions
• Error handling: Invalid inputs and how agent recovers

Document behavior including tool usage, response quality, and accuracy.

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Creating Test Cases

Define expected behavior for validation: What to test:

• Task completion accuracy
• Tool selection and usage
• Response format consistency
• Error handling
• Response time

Example test: “Extract key information about Apple Inc.”

• Expected tools: web_search
• Required fields: name, industry, headquarters
• Success: All fields present and accurate

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Batch Testing

Test agents on sample datasets before production use.

1. Create test dataset with 10-50 records
2. Run agent on test data
3. Review output quality and accuracy
4. Fix issues and retest
5. Deploy when quality meets standards

Compare outputs against expected results to catch issues early.

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Performance Validation

Speed: Simple queries < 2 seconds, complex analysis < 30 seconds Quality: Check response relevance, accuracy, completeness, and clarity Consistency: Same input should yield similar results across tests Monitor resource usage and tool call patterns for optimization opportunities.

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Common Issues

Inconsistent results: Model variability is normal. Run multiple tests to identify patterns. Slow performance: Review tool usage, consider caching frequent queries, optimize system prompt. Accuracy problems: Refine system prompt instructions, adjust tool configurations, add examples. Tool failures: Verify API credentials, check rate limits, test connectivity.

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Production Testing

Gradual rollout: Test on small dataset subset first, then expand gradually. Monitor continuously: Track accuracy, response times, error rates in production. User feedback: Collect and review user reports to identify improvement areas.

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Next Steps