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Advanced

The Advanced page allows you to configure key parameters of your AI Agent, including selecting a language model, setting prediction confidence, and defining inactivity timeouts.

1. Select Your Own LLM​

This section lets you choose an integrated Language Model (LLM) to power your AI Agent.

  • LLM Provider: Select an available provider (e.g., Moveo.AI, OpenAI, Azure OpenAI, Anthropic, Gemini, DeepSeek).
  • Model: After selecting a provider, choose a specific model that best fits your needs. For example, if you select OpenAI, you might choose a model like o1-preview or gpt-4-turbo; if you choose Anthropic, you might opt for claude-3-5-sonnet-20240620.

2. Prediction Threshold​

This setting controls the confidence level required for an intent prediction to be considered correct. The threshold ranges from 0 to 1.

  • Lower threshold (more flexible, but increases false positives)
    In customer support scenarios, a lower threshold allows the AI Agent to be more responsive. Even if it’s uncertain about a user’s intent, it will still attempt a relevant response. This helps keep conversations flowing but may sometimes result in incorrect predictions.

  • Higher threshold (more conservative, requiring clearer user input)
    In fields like legal or financial assistance, a higher threshold ensures only the most confident predictions are accepted. This reduces errors but may require users to phrase their questions more precisely for accurate responses.

3. Inactivity Timeout​

This setting determines how long the AI Agent retains context before resetting due to user inactivity.

  • Available options: 5 min, 15 min, 30 min, 45 min, 1 hour, 2 hours, 8 hours, 24 hours.

Adjusting this setting optimizes the AI Agent’s performance based on your workflow requirements.

  • Shorter timeouts (e.g., 5–30 min) → Ideal for fast-paced interactions where outdated context should be discarded quickly. This prevents the AI Agent from using stale information that might mislead the conversation.
  • Longer timeouts (e.g., 2, 8, 24 hours) → Useful for cases where conversations span extended periods. For example, in complex troubleshooting or long-running customer support sessions, a longer timeout allows the AI Agent to maintain context even if the user returns hours later.
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