Unlike image recognition or NLP, clinical trial data possesses a unique and complex mathematical geometry. According to Dr. Juraji, this means generic AI models are insufficient. Solving trial failures requires specialized AI built to navigate this specific, difficult data landscape.

Frontier models can be slow, expensive, and prone to hallucinating across non-deterministic training sets. Training a small language model exclusively on a verified proprietary corpus provides sub-second latencies, cuts operational compute costs, and matches or exceeds premier frontier models on domain tasks.

While guardrails in prompts are useful, a more effective step to prevent AI agents from hallucinating is careful model selection. For instance, using Google's Gemini models, which are noted to hallucinate less, provides a stronger foundational safety layer than relying solely on prompt engineering with more 'creative' models.

You can't just deploy a probabilistic model like an LLM in a high-stakes field like healthcare. The key is to build a deterministic infrastructure (e.g., a rules engine with clinical guidelines) that governs the AI's operation, ensuring it operates safely within predefined constraints.

Instead of building a single, monolithic AI agent that uses a vast, unstructured dataset, a more effective approach is to create multiple small, precise agents. Each agent is trained on a smaller, more controllable dataset specific to its task, which significantly reduces the risk of unpredictable interpretations and hallucinations.

An ideal workflow separates probabilistic and deterministic tasks. Use an LLM agent for the creative front-end: helping users identify business constraints, research regulations, and formulate the problem. The agent then calls a dedicated mathematical optimization engine to generate a guaranteed, reliable, and explainable solution.

Jurgi Camblong cautions against the hype that Large Language Models (LLMs) can solve every problem in medicine. Sophia Genetics uses a diverse "toolbox" of AI—including statistical methods and machine learning—selecting the most efficient mathematical model for a specific biological problem and dataset.

For most enterprise tasks, massive frontier models are overkill—a "bazooka to kill a fly." Smaller, domain-specific models are often more accurate for targeted use cases, significantly cheaper to run, and more secure. They focus on being the "best-in-class employee" for a specific task, not a generalist.

For critical enterprise functions like financial modeling, 99.9% accuracy from a probabilistic LLM is unacceptable. Platforms like Salesforce's Agent Force 360 solve this by layering deterministic logic and guardrails on top of the AI, ensuring compliance and preventing costly errors where even a 0.1% failure rate is too high.