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.

Avoid storing sensitive data like contracts directly within your custom-built agent. Instead, use "agent hopping": have the AI call APIs to a secure system of record, like Salesforce, to access data on-demand. This adds a crucial security layer and prevents data liability.

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.

Reframe hallucinations as signals of poor data quality or retrieval, not model failures. The AI is improvising because you failed to provide the correct script—the authoritative information, or 'canon.' This shifts focus from blaming the model to fixing your data pipeline.

To solve for AI hallucinations in high-stakes decisions, advanced platforms use the LLM as an interpreter that writes code to query raw data. If data is unavailable, it returns an error instead of fabricating an answer, making every analysis fully auditable and grounded in verifiable data.

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.

A key principle for reliable AI is giving it an explicit 'out.' By telling the AI it's acceptable to admit failure or lack of knowledge, you reduce the model's tendency to hallucinate, confabulate, or fake task completion, which leads to more truthful and reliable behavior.

To deploy LLMs in high-stakes environments like finance, combine them with deterministic checks. For example, use a traditional algorithm to calculate cash flow and only surface the LLM's answer if it falls within an acceptable range. This prevents hallucinations and ensures reliability.

To prevent AI agents from over-promising or inventing features, you must explicitly define negative constraints. Just as you train them on your capabilities, provide clear boundaries on what your product or service does not do to stop them from making things up to be helpful.