A divide exists in AI agents, characterized by deterministic versus non-deterministic approaches. Initially, the expectation was that AI agents could emulate human reasoning to autonomously navigate tasks, particularly in contexts like healthcare. Deterministic agents yield consistent outputs, similar to APIs, while non-deterministic agents, like deep research models, produce variable results each time. In healthcare, for example, checking claim statuses often demands navigating varied payer responses and workflows. Although a non-deterministic approach might seem more suitable due to its flexibility, the high need for context and the risk of inconsistencies make determinism preferable at enterprise scale, especially where compliance and precision are crucial. While consumer applications and voice agents may benefit from non-deterministic methods for personalization and adaptability, the enterprise sector favors predictable outcomes. A hybrid strategy leveraging both approaches can yield optimal results, balancing the need for consistent performance with the capability to handle variability.
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June 2025: The Great AI Agent Divide

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