A computer-implemented method of automatically configuring at least one action determination model for an agent device of at least one agent connected to at least one simulated Information Technology (IT) environment via a computer network, the agent device of the at least one agent having the at least one action determination model indicating at least one action option to be simulated for execution by the agent device of the at least one agent to the at least one simulated IT environment, the method comprising:
- generating and configuring, via a processor, the at least one simulated IT environment comprising a plurality of computer-virtualized and/or real networked IT systems interconnected via a defined network topology for performing IT-system operations, wherein the at least one simulated IT environment models hardware and software components of real-world IT systems;
- storing the at least one action determination model in a storage device, each action determination model including a plurality of machine-learning (ML) based artificial intelligent (AI) neural net models each comprising an IT-system operation and defining at least one action to be simulated for execution by the agent device of the at least one agent to the at least one simulated IT environment in at least one given context of attributes, state and/or behaviors of the at least one simulated IT environment for performing the IT-system operation by the IT systems in the at least one simulated IT environment and having a different set of weighted parameters for a corresponding one of the at least one action, the IT-system operation modifying a state of at least one component within the simulated IT environment, including system configuration changes, resource allocation adjustments, log file manipulations, and network traffic modifications;
- defining at least one objective of the at least one agent to be performed, to the at least one simulated IT environment, by the agent device of the at least one agent and the at least one action option to be simulated for execution by the agent device of the at least one agent to perform the at least one objective;
- repeatedly simulating, using a simulation algorithm executed by the processor, execution of the at least one action by the agent device of the at least one agent to the at least one simulated IT environment, and obtaining at least one simulated result of the simulated execution of the at least one action;
- after each simulation of the execution of at least one action, detecting a deviation between the at least one simulated result of the simulated execution of the at least one action to the at least one simulated IT environment and the at least one objective of the at least one agent, and dynamically reconfiguring, via the processor, in response to the detected deviation, the at least one action determination model by replacing a first one of the plurality of ML-based AI neural net models having a first set of weighted parameters and stored in the storage device with a second one of the plurality of ML-based AI neural net models being different and separately stored from the first one of the plurality of ML-based AI neural net models and having a second set of weighted parameters different from the first set of weighted parameters, until the simulated result satisfies the at least one objective based on at least one of system performance metrics, network and software outputs and responses, error rates, and execution logs, thereby reducing the deviation in subsequent simulations,
- wherein replacing the first one of the plurality of ML-based AI neural net models with the second one of the plurality of ML-based AI neural net models occurs as part of an interactive deviation-reduction loop.