Reliability and predictability
The best way we work together with computer systems at this time is predictable. For example, after we construct software program techniques, 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. Quite, we lead with the result we wish to obtain, and the agent determines the way to attain this purpose. The software program agent has a level of autonomy, which suggests there might be some randomness within the outputs.
We noticed the same challenge with ChatGPT and different LLM-based generative AI techniques once they first debuted. However within the final two years, we’ve seen appreciable enhancements within the consistency of generative AI outputs, because of fine-tuning, human suggestions loops, and constant efforts to coach and refine these fashions. We’ll must put the same degree of effort into minimizing the randomness of agentic AI techniques to make them extra predictable and dependable.
Information privateness and safety
Some firms are hesitant to make use of agentic AI as a result of privateness and safety considerations, that are much like these with generative AI however might be much more regarding. For instance, when a consumer engages with a giant language mannequin, each bit of knowledge given to the mannequin turns into embedded in that mannequin. There’s no means to return and ask it to “overlook” that data. Some kinds of safety assault, equivalent to immediate injection, make the most of this by attempting to get the mannequin to leak proprietary data. As a result of software program brokers have entry to many alternative techniques with a excessive degree of autonomy, there’s an elevated threat that it may expose non-public information from extra sources.