Agentic AI: The Top Challenges and How to Overcome Them

Before deploying agentic AI, enterprises should be prepared to address several issues that could impact the trustworthiness and security of the system.

As generative AI continues to surge in popularity, we are already seeing it evolve into the next generation of machine-learning-driven technology: agentic AI.

With agentic AI, we are not just prompting models and receiving an answer in a simple one-step process. The AI is engaging in complex multi-step processes, often interacting with different systems to achieve a desired outcome. For example, 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.

Agents will be one of the most significant innovations in the AI industry, possibly more impactful than future generations of foundation models. By 2028, Gartner predicts that at least 15% of day-to-day work decisions will be made autonomously by agentic AI, up from 0% in 2024.

Although AI agents promise to improve efficiency, save costs, and free up IT staff to focus on more critical projects that need human reasoning, they’re not without challenges. Before deploying agentic AI, enterprises should be prepared to address several issues that could otherwise impact the trustworthiness and security of the systems and outputs.

Model logic and critical thinking

With agentic AI, one agent acts as a “planner” and orchestrates the actions of multiple agents. The model provides a “critical thinker” function, offering feedback on the output of the planner and the different agents that are executing on those instructions. The more feedback created, the more insights the model gains, and the better the outputs will be.

For agentic AI to work well, the critical-thinker model needs to be trained on data that’s as closely grounded in reality as possible. In other words, we need to give it a lot of information on specific goals, plans, actions, and results, and provide a lot of feedback. This could require many iterations — running through hundreds or even thousands of plans and results — before the model has enough data to start acting as a critical thinker.[…]