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1. Understand the Issue

• Reproduce the Problem: Try to replicate the issue by providing the same input or queries to the AI Agent. Take note of where the responses start to become problematic.

• Identify Patterns: Is the issue occurring with specific types of questions, keywords, or contexts? Understanding these patterns can help narrow down the problem.

2. Examine the Input

• Input Validation: Check if the input to the model is well-formed. Incorrect or ambiguous input can cause inaccurate or nonsensical responses.

• Preprocessing: Ensure that any input preprocessing (like tokenization, normalization, or encoding) is functioning as expected.

3. Check Model Outputs

• Logging Responses: If the platform supports logging, review the raw output from the model. This allows you to track how the model interprets the input and generates the response.

• Response Quality: Analyze whether the model's output is coherent, relevant, and follows the expected conversational flow.

4. Evaluate Data Used for Training

• Training Data: Check if the training data is diverse and comprehensive enough to cover the types of queries you're encountering. Insufficient training data may lead to incorrect or vague responses.

• Biases and Gaps: Look for any patterns that indicate the model is failing in certain areas, which could point to biases or gaps in the data.

5. Model Fine-tuning

• Parameter Tuning: If you're working with a model that can be fine-tuned, you might need to adjust parameters or retrain the model on a more specific dataset that reflects the desired output.

• Error Feedback: Continuously provide feedback on incorrect responses to improve model performance over time. Some platforms support an active learning approach to refine the model.

6. Use Debugging Tools

• Logging and Monitoring: Implement tools to monitor model behavior, like logging frameworks, error tracking, and performance analytics. This can help pinpoint where things go wrong.

• Visualization: Use tools like attention visualization or layer-wise analysis to understand how the model processes the input and produces outputs.

7. Evaluate Context and Memory

• Contextual Understanding: If the AI Agent is failing to maintain conversation context or provide accurate follow-up answers, evaluate how it is managing context and memory.

• State Management: Make sure the system is correctly tracking state between user inputs. If the model is not retaining information across turns, responses may become disjointed or irrelevant.

8. Examine API Calls (if applicable)

• API Responses: If your AI Agent uses external APIs to generate answers, check the responses from those APIs. Sometimes the issue lies outside the AI Agent itself, in the data being pulled or the way it is processed.

• Error Handling: Ensure proper error handling for situations when the API is down or returns an unexpected result.

9. Test with Different User Personas

• User Diversity: Try running tests with different types of user personas to see if certain demographics or question types lead to poorer results.

• Simulate Different Scenarios: Input different user intents and emotional tones to ensure that the AI Agent is adaptable and can handle diverse interactions.

10. Collaborate with Support

• Agent Supply Support: If you’re unable to resolve the issue, consider reaching out to Agent Supply's support team. They may have additional tools or insights into debugging your specific implementation.

By systematically following these steps, you should be able to identify the source of the issue and improve the AI Agent's performance. If you're working within Agent Supply's specific platform, there may also be documentation or community resources that can provide more tailored advice.