The Rise of AI Agents: Harnessing Autonomous AI to Drive Business Results
Introduction
As the sizzle of generative AI wanes, organizations realize that artificial intelligence (AI) is not the panacea they once thought. Although there is tremendous upside to adopting AI technology, the systems are complex, expensive, and risky. Even though generative AI systems can seemingly generate unlimited content, business and IT leaders need to implement generative AI on a use-case-by-case basis. And on top of that, their abilities (or skills) are relatively narrow. Whether embedded in a customer service chatbot or summarizing medical records, each application is unique and requires development, maintenance, monitoring, guardrails, training, and new organizational processes to ensure efficacy, accuracy, and robustness.
One critical issue that companies must address is how to add their organizational data to provide context to the large language models (LLMs). Techniques like clever prompt engineering, fine-tuning, and retrieval augmented generation (RAG) can all help reduce ‘hallucinations’ — the generation of incorrect or misleading information — and inform the output responses.
