Graphical editor for complex artificial intelligence agent systems

A system, comprising:

• one or more processors;
• and
• memory storing thereon instructions that, as a result of being executed by the one or more processors, cause the system to:
• detect through a graphical user interface a request to generate an automated agent, wherein the request includes a graphical representation of a set of nodes and a set of connections amongst the set of nodes, wherein the set of nodes corresponds to different machine learning models and a set of functions performable by the different machine learning models, and wherein the set of connections defines processing paths of the automated agent;
• dynamically convert the graphical representation of the set of nodes and the set of connections into programmatic code;
• execute the programmatic code to evaluate a performance of the different machine learning models in processing the set of functions according to the processing paths;
• update the graphical user interface according to the performance of the different machine learning models;
• detect through the graphical user interface to implement the automated agent according to the graphical representation of the set of nodes and the set of connections;
• convert the programmatic code into a configuration file;
• and
• expose an application programming interface (API) endpoint corresponding to the configuration file, wherein the API endpoint is exposed through a runtime environment (RTE), and wherein the RTE processes API calls to the API endpoint to execute the automated agent according to the configuration file, the set of functions, and the processing paths.

A non-transitory computer-readable storage medium storing thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to:

• detect through a graphical user interface a request to generate an automated agent, wherein the request includes a graphical representation of a set of nodes and a set of connections amongst the set of nodes, wherein the set of nodes corresponds to different machine learning models and a set of functions performable by the different machine learning models, and wherein the set of connections defines processing paths of the automated agent;
• dynamically convert the graphical representation of the set of nodes and the set of connections into programmatic code;
• execute the programmatic code to evaluate a performance of the different machine learning models in processing the set of functions according to the processing paths;
• update the graphical user interface according to the performance of the different machine learning models;
• detect through the graphical user interface to implement the automated agent according to the graphical representation of the set of nodes and the set of connections;
• and
• convert the programmatic code into a configuration file;
• and
• expose an application programming interface (API) endpoint corresponding to the configuration file, wherein the API endpoint is exposed through a runtime environment (RTE), and wherein the RTE processes API calls to the API endpoint to execute the automated agent according to the configuration file, the set of functions, and the processing paths.