As generative AI continues to evolve, the next generation of machine-learning-driven technology is emerging: agentic AI. This technology enables complex multi-step processes, often interacting with different systems to achieve a desired outcome. According to Gartner, by 2028, at least 15% of day-to-day work decisions will be made autonomously by agentic AI, up from 0% in 2024.

Agentic AI has the potential to improve efficiency, save costs, and free up IT staff to focus on more critical projects that require human reasoning. For instance, an organization could have an AI-powered help desk with agents that use natural language processing to understand and process incoming IT support tickets from employees. These agents could autonomously reset passwords, install software updates, and elevate tickets to human staff when necessary.

However, before deploying agentic AI, enterprises should be prepared to address several issues that could impact the trustworthiness and security of the system. One of the significant challenges is model logic and critical thinking. The critical-thinker model needs to be trained on data that's as closely grounded in reality as possible, requiring many iterations and feedback loops to start acting as a critical thinker.

Another issue is reliability and predictability. Unlike traditional software systems, agentic AI processes do not provide step-by-step instructions, leading to some randomness in the outputs. To address this, companies need to put a similar level of effort into minimizing the randomness of agentic AI systems to make them more predictable and reliable, similar to the improvements seen in generative AI outputs.

Data privacy and security are also significant concerns. Because software agents have access to many different systems with a high level of autonomy, there is an increased risk that it could expose private data from more sources. To address this, companies need to start small, containerizing the data as much as possible, and anonymize the data, obscuring the user and stripping any personally identifiable information from the prompt, before sending it to the model.

Data quality and relevancy are also crucial. Agentic AI models need to deliver results that are grounded in quality data that is relevant to the user's prompt. This can be achieved by using data streaming platforms that provide engineers with tools to enable relevant answers using high-quality data.

Finally, ROI and talent are significant hurdles. AI is still new territory for many companies, requiring significant investments in hardware, infrastructure, and talent. However, the potential impact on the business may be much greater than what they're seeing with just generative AI.

Despite these challenges, agentic AI is expected to spread through enterprises much like generative AI has. Companies like GitHub Copilot are already moving in this direction, evolving from simply automating certain coding processes to acting in an agentic way to write and test code.

In conclusion, agentic AI has the potential to revolutionize the way enterprises operate, but it's crucial to address the trustworthiness and security concerns before deployment. With careful planning, investment, and talent acquisition, companies can unlock the benefits of agentic AI and stay ahead of the curve.