Ask any GenAI agent a question, and you risk receiving an inaccurate response or hallucination. AI hallucinations pose significant risks to enterprises in numerous costly ways. According to a recent Vectara study, AI hallucinations happen between 0.7% and 29.9% of the time, depending on the large language model used.The impact of hallucinations can disrupt operations, erode efficiency and trust, and cause costly setbacks. Customer and stakeholder trust is swiftly eroded if the public discovers that an enterprise relied on false information, such as a bank chatbot offering incorrect loan terms, AI robo-advisors recommending inappropriate investments, or misinformation emanating from call centers.The Real-World Risks of AI HallucinationsA recent example occurred when we engaged with a leading manufacturer of mechanical and electronic devices. Their customer support team operated in a high-churn environment with representatives of varying experience levels handling numerous daily calls about device troubleshooting. The primary issue arose when the AI assistant exhibited “over-eager” behavior, offering troubleshooting recommendations for products not included in its knowledge base. This resulted in the AI extrapolating solutions from similar products or generating hallucinated responses. This was particularly problematic given the highly nuanced nature of the manufacturer’s devices, where seemingly identical products could have significant functional differences.To address the hallucination problem, we implemented a comprehensive approach focused on precision and validation. The team developed additional checks and guardrails within the AI agent to ensure more accurate analysis of product variants during knowledge base searches. These safeguards were designed to guarantee that the knowledge base segment used for troubleshooting matched the specific device in question with high precision. Furthermore, the solution incorporated additional validation steps into the troubleshooting workflow, creating multiple checkpoints before the AI could make recommendations to call center representatives through the user interface.The implementation of enhanced guardrails and validation processes successfully addressed the hallucination issue while delivering measurable improvements in customer support operations. The refined AI assistant now provides more accurate, device-specific troubleshooting guidance, significantly reducing misdiagnosis rates. The additional precision controls ensure that recommendations are based on exact product matches rather than potentially misleading extrapolations. As a result, the client experienced faster issue resolution times, improved support quality, and enhanced confidence in the AI-assisted troubleshooting process, ultimately creating a more effective support environment for both representatives and customers.How Agentic AI Is Used To Prevent AI HallucinationsAI Agents are becoming a common approach to mitigating the risk of AI hallucinations. With their ability to perceive their environment, reason about possible actions, act to achieve objectives, and learn from feedback to improve their performance over time, continuously.As in our call center example, Agentic AI can evaluate whether a response aligns with known facts or logical reasoning before finalizing an answer, a self-assessment technique performed by AI agents. This iterative reasoning helps identify and correct errors internally. Agentic AI also retrieves real-time information from verified external resources rather than relying on pre-trained data. This ensures responses are current and accurate. Taking this concept further, AI agents can cross-reference multiple sources or run queries across different datasets to confirm the consistency and reliability of their answers, reducing the chances of information being fabricated.Agentic AI models can also employ Chain-of-Thought Prompting, which breaks down complex problems into logical steps or a chain-of-thought reasoning; a technique that is especially effective in reasoning-intensive tasks.Finally, leveraging Retrieval-Augmented Generation (RAG) solutions has proven to be helpful. Agentic RAG combines the strengths of retrieval-augmented generation with autonomous agentic reasoning, enabling the AI to manage complex tasks, personalize responses, and prioritize relevant information — all while grounding outputs in trusted knowledge sources.The Myth of Data Quality vs. Data QuantityWhile AI requires massive amounts of data, it is a false assumption that more data leads to hallucinations. The relationship between data volume and AI hallucinations is nuanced, depending heavily on the quality and type of data. While enterprises often assume “more data = better AI,” the critical factors are verification (removing inaccuracies), source control (prioritizing human-generated data), and diversity (avoiding domain overrepresentation). When these conditions are met, scaling data improves reliability; without them, hallucinations worsen.Key Strategies for Preventing AI HallucinationsWhile hallucinations cannot be eliminated entirely, there are strategies to undertake that substantially mitigate their occurrence and impact.Not surprisingly, ensuring the AI model is trained on diverse, accurate, and comprehensive datasets minimizes the risk of hallucinations. Regularly updating and cleaning training data enables the model to adapt to new information and avoid producing outdated or incorrect outputs. Crafting clear, specific, and detailed prompts guides the AI toward more accurate responses and reduces ambiguity, which can otherwise lead to inaccurate or misleading results.Incorporating human oversight, especially in high-stakes or regulated environments, enables experts to validate and correct AI-generated outputs before they are used or published, serving as a final safeguard against errors and inaccuracies. Clearly defining the intended use and boundaries of the AI system ensures that it is applied only to tasks for which it was designed and trained, thereby minimizing the risk of hallucinations when facing unfamiliar scenarios.The post Agentic AI Is Key To Preventing Costly AI Hallucinations appeared first on The New Stack.