How self-learning AI agents will reshape operational workflows

Why are AI agents well-suited to this new era of learning?

While LLMs are great at collecting and digesting operational information, AI agents that learn from experience can act and react to the outcome of an action, then learn from its aftermath. In an operations management context, this also allows the agent to try alternative remediation options based on the signals it receives.

Experience-based training for AI agents offers strong potential because it allows agents to act autonomously in real-world situations, guided by rewards that emerge from the environment. In the context of operations management, this means agents can learn from past incidents, events, customer tickets, application and infrastructure metrics and logs, as well as any other metrics made available to them.

While modern-day hype cycles demand rapid results, much of the promise of AI agents lies in how they will improve operations management over time. Given enough time and training data, the AI agent will be able to plan actions and predict their consequences in the environment—i.e., predict the reward—much better than a human. This will allow organizations to minimize human intervention in digital operations, freeing them up to focus on higher-value innovation work.