Reliability and predictability
The way in which we work together with computer systems right this moment is predictable. As an illustration, once we construct software program methods, an engineer sits and writes code, telling the pc precisely what to do, step-by-step. With an agentic AI course of, we don’t present step-by-step directions. Somewhat, we lead with the end result we need to obtain, and the agent determines find out how to attain this objective. The software program agent has a level of autonomy, which suggests there might be some randomness within the outputs.
We noticed the same situation with ChatGPT and different LLM-based generative AI methods once they first debuted. However within the final two years, we’ve seen appreciable enhancements within the consistency of generative AI outputs, due to fine-tuning, human suggestions loops, and constant efforts to coach and refine these fashions. We’ll have to put the same stage of effort into minimizing the randomness of agentic AI methods to make them extra predictable and dependable.
Knowledge privateness and safety
Some firms are hesitant to make use of agentic AI resulting from privateness and safety considerations, that are much like these with generative AI however might be much more regarding. For instance, when a person engages with a giant language mannequin, each bit of knowledge given to the mannequin turns into embedded in that mannequin. There’s no manner to return and ask it to “neglect” that info. Some varieties of safety assault, resembling immediate injection, make the most of this by attempting to get the mannequin to leak proprietary info. As a result of software program brokers have entry to many alternative methods with a excessive stage of autonomy, there may be an elevated threat that it might expose non-public knowledge from extra sources.
