AI agents are autonomous systems or programs designed to perform tasks, make decisions, or interact with their environment, often without continuous human intervention. They can be specialized to serve various purposes, such as virtual assistants, chatbots, recommendation engines, or autonomous vehicles. 

AI agents are at the core of modern AI applications, allowing systems to function autonomously, optimize processes and enhance user experiences. They are becoming increasingly sophisticated with the advancement of technologies like deep learning, reinforcement learning and NLP. The use of agent strategies is growing rapidly as they are an efficient means to lower costs of training and inference by more closely targeting intended tasking. 

Improving AI Agents

Advanced AI Agents today use learning and feedback mechanisms for learning from their actions. This learning process improves the performance of the agent over time. Many methods are used:

• For Rule-based agents, decisions are based on a set of pre-defined rules.
• For ML-based agents, decisions are based on the patterns learned during training, using statistical methods to make predictions or decisions.
• For Reinforcement-trained agents, learning agents adjust their strategy based on the rewards or penalties they receive. They evaluate their current state, potential actions and expected rewards.

Even so, all current Agent development is a direct result of statically designed, neural network architectures and training sets that underlie them, making them perfect for inclusion of Config’s Self-Aware™ and AdaptiveAI™ technologies.

After all, agents are just task specific complex systems in their own right. Using the tools of the Sequitur™ Platform, a Self-Aware™ agent can easily be adapted to use AdaptiveAI™ computing technologies to dynamically reason about its performance to goals and adjust its activities to optimally meet those goals at each instant. Goals can be used to set SLAs with users to maintain cost targets, set response accuracy or be applied to automated reinforcement learning techniques to target desired reward structures dynamically. 

In fact, goals can be as diverse and applied as developers develop their own application requirements for performance and control, by running as a first-class software objects under system control, developers also gain the significant additional benefit of easily being able to change goals to apply to different systems or meet changing run-time needs.