"Agents are a fundamentally different paradigm to predictive and generative AI. What sets them apart, aside from being multimodal and capable of deep reasoning, is their autonomous nature. It sounds deceptively simple, but when software has agency - the ability to make decisions and take actions on its own - the results can be quite profound. This creates a fundamental challenge for companies integrating AI software, which is traditionally built for deterministic, predictable workflows."
"Agentic AI is inherently probabilistic - the same input can produce different outputs, and agents may take unexpected paths to reach their goals. This mismatch between deterministic infrastructure and probabilistic behavior creates new design challenges around governance, monitoring, and user trust. These aren't just theoretical concerns, they're already playing out in enterprise"
Agentic AI introduces autonomous software agents capable of multimodal input processing and deep reasoning. These agents make decisions and take actions independently, producing probabilistic outcomes where identical inputs can lead to different outputs. Traditional enterprise infrastructure and product design assume deterministic, predictable workflows and thus face a mismatch with agentic behavior. The probabilistic nature of agents raises new requirements for governance, continuous monitoring, and mechanisms to maintain user trust. Enterprises must adapt tooling, testing, and operational practices to manage unexpected agent paths and ensure accountability. Design principles must evolve to address autonomy, non-determinism, and safety at scale.
Read at Medium
Unable to calculate read time
Collection
[
|
...
]