Can AI agents really make decisions in high-stakes industrial environments?Generative AI agents, on their own, do not have a robust understanding of cause-and-effect for real-world decision-making.However, when combined with Deep Reinforcement Learning, AI agents gain the ability to reason, learn from interaction, and make decisions that solve operational problems in complex, real-world environments, like the plant floor. Case in point.Bryan DeBois and his team at RoviSys developed an Autonomous AI agent to manage a notoriously difficult glass bottle production process, where small disruptions like temperature fluctuations can quickly push the process out of specification.Here’s how they approached it:✅ 𝐒𝐭𝐞𝐩 1 - 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐓𝐞𝐚𝐜𝐡𝐢𝐧𝐠They captured the knowledge and decision-making strategies of expert human operators and used this to train the AI agent, essentially teaching it how to respond to different operating conditions.✅ 𝐒𝐭𝐞𝐩 2 - 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 𝐌𝐨𝐝𝐞Initially, the agent didn’t control the process directly. It simply made recommendations. Operators reviewed the suggestions and gave feedback using a simple green/red button system. This built trust and allowed the team to validate the AI’s decisions without risk.✅ 𝐒𝐭𝐞𝐩 3 - 𝐂𝐥𝐨𝐬𝐞𝐝 𝐋𝐨𝐨𝐩 𝐂𝐨𝐧𝐭𝐫𝐨𝐥Only after months of successful operation in support mode did they enable full automation. Even then, strict safety measures were in place:⇨ Limited control authority⇨ Clearly defined operating boundaries⇨ Automatic handover to human operators if conditions exceeded the agent’s trainingThe Results:⇨ Human operators typically needed 7–20 minutes to bring the process back into spec⇨ The AI agent consistently did it in under 5 minutes⇨ And it maintained safety by operating strictly within validated limitsIn the latest episode of the AI in Manufacturing podcast, I sat down with Bryan, Director of Industrial AI at RoviSys, to dive deeper into how manufacturers can leverage AI and autonomous agents to optimize manufacturing operations and improve efficiency