Click to learn more about Agentic AI and how people fit in with Hugo Farinha's article on Forbes.
Agentic AI is on track, but it has yet to pull into the station. The stall in the AI curve means agentic AI is being greeted as the missing link and augmentation through different types of AI, playing together to share skills, which is very attractive from a productivity point of view.
So let’s address the thorny issue of what agentic AI actually is: taking processes and tasks that a person would normally need to perform, where decisions would need to be made by a human on the outputs that preclude automation, and involving GenAI plus RPA and automation to fulfill it. Different agents specialize in doing different tasks, with some focused on compliance and standards. Some fulfill user requests; some seek out, collect and redistribute data to the right places. Workflow agents identify APIs as well as generate and execute workflows across applications.
As an example, in our company's world of QA testing, when tests are running and going through a new version of the application, there are AI agents running autonomously, making decisions. If the code behind the "button" has changed, these agents will be able to make real-time decisions on whether to fix this and keep running or stop the process. This is real-time diagnostics and fixing.
No one’s cowering before their robot overlords, though. Humans are still 100% going to be needed, but their roles will change and be less siloed. There will be fewer of them, too. One action from a team will have the potential to affect other teams much more quickly, making supervision of AI agents, roles involving training AI models and human oversight of critical decision making absolutely vital.
So, what might these new roles look like?
Read more of Hugo Farinha's article on what Agentic AI is and isn't on Forbes.
