Airia LLC

Systems and methods are described for building artificial intelligence (“AI”) agents. A server can provide a user interface (UI) that includes options to select and connect various agent objects. The agent objects can include prompt objects, dataset objects, model objects, and one or more code objects. A subset of the agent objects can be identified as available for selection based on evaluation of at least one management policy associated with an administrative user. A manifest file is generated based on the connected agent objects, and the manifest is validated against dependency rules to ensure that the stages of the agent meet prerequisites for the stages. Then, the server performs a simulated execution of an agent that corresponds to the validated manifest file, including an identification of at least one execution metric associated with the simulated execution.

The present invention provides a system for processing user queries through an artificial intelligence (“AI”) pipeline, utilizing data chunking, question generation, and AI models to deliver contextually relevant responses. The system includes a server that ingests and chunks datasets, generates vector embeddings, and stores the data in one or more vector databases. An execution engine sends the chunked data to an AI model that generates potential questions tailored to different user personas. These questions, along with their corresponding data chunks, are stored in the database for future retrieval. When a user submits a query, the system semantically compares the query to the pre-generated question vectors and retrieves the most relevant question and associated data chunk. The query is then sent to an external AI model for final response generation. The system provides seamless interaction, delivering optimized, context-aware responses to user queries in real-time.

The system provides a permissions engine derives permissions for responses from artificial intelligence (AI) agents. The permissions are derived based on the access permissions of chunks used to generate the response, and the response is cached for reuse with users meeting the permissions requirements. The system can also derive an access permission level for a user attempting to access the cached result or other content. This can be based on a managed access profile that includes user behavior criteria and device criteria. Likewise, the system can validate and derive sensitivity levels of documents that are ingested for use in AI agents and discriminate between chunks to use in synthesizing results based on the derived permissions of the chunks and their relationship to the user.

The system provides a permissions engine derives permissions for responses from artificial intelligence (AI) agents. The permissions are derived based on the access permissions of chunks used to generate the response, and the response is cached for reuse with users meeting the permissions requirements. The system can also derive an access permission level for a user attempting to access the cached result or other content. This can be based on a managed access profile that includes user behavior criteria and device criteria. Likewise, the system can validate and derive sensitivity levels of documents that are ingested for use in AI agents and discriminate between chunks to use in synthesizing results based on the derived permissions of the chunks and their relationship to the user.

Systems and methods are described for comparing execution of two or more artificial intelligence (AI) AI agents. A platform can provide a user interface (UI) that allows for selection or creation of multiple AI agents. The AI agents can utilize different agent objects, such as different prompts, datasets, or models. The AI agents can be displayed on a single UI screen, where execution of the AI agents is simultaneously simulated. The same inputs can be provided to the multiple AI agents, and the corresponding outputs can display on screen. The platform can also vectorize and compare the semantic similarity of the outputs, presenting an indication of the semantic similarity on the same UI screen.

Systems and methods are described for comparing execution of two or more artificial intelligence (AI) AI agents. A platform can provide a user interface (UI) that allows for selection or creation of multiple AI agents. The AI agents can utilize different agent objects, such as different prompts, datasets, or models. The AI agents can be displayed on a single UI screen, where execution of the AI agents is simultaneously simulated. The same inputs can be provided to the multiple AI agents, and the corresponding outputs can display on screen. The platform can also vectorize and compare the semantic similarity of the outputs, presenting an indicating of the semantic similarity on the same UI screen.

The integration and deployment of machine learning (ML) models in configurable workflows is described. An example method of deployment of an ML model includes receiving a model submission package from a client device, extracting configuration parameters from a manifest file, provisioning a compute environment within the artificial intelligence (AI) platform based on the configuration parameters, instantiating and rendering an agent object within a graphical workflow canvas, and enabling, by the graphical workflow canvas, a user to define an executable workflow comprising the agent object, such that the executable workflow is invoked by the AI platform. The executable workflow can be invoked by the AI platform to perform model inference or service execution within the defined workflow.

The present invention provides a system for processing user queries through an artificial intelligence (“AI”) pipeline, utilizing data chunking, question generation, and AI models to deliver contextually relevant responses. The system includes a server that ingests and chunks datasets, generates vector embeddings, and stores the data in one or more vector databases. A pipeline engine sends the chunked data to an AI model that generates potential questions tailored to different user personas. These questions, along with their corresponding data chunks, are stored in the database for future retrieval. When a user submits a query, the system semantically compares the query to the pre-generated question vectors and retrieves the most relevant question and associated data chunk. The query is then sent to an external AI model for final response generation. The system provides seamless interaction, delivering optimized, context-aware responses to user queries in real-time.

Air purification devices are disclosed that cleanse air of germs and microbes through an oxidation process using an ultraviolet light source that causes a chemical reaction with a proximate catalyst-coated surface. The devices use a replaceable ionization cell folded into a core assembly and positioned around the ultraviolet light source. The ionization cell is coated with the reactive catalyst on at least an inner surface facing the light source and air is cleansed as it passes through the ionization cell. The devices provide safety mechanisms to protect against human exposure to the ultraviolet light.

The system securely executes model context protocol (“MCP”) processes for artificial intelligence (“AI”) agents by creating nano sandboxes in which the MCP processes execute. A server-sent events (“SSE”) bridge identifies a request to initialize an SSE channel with an MCP server. The SSE bridge instantiates a nano sandbox based on a definition extracted from the request, creating a custom and dynamic isolated execution environment. An MCP process is launched within the nano sandbox. The SSE bridge returns a session identifier, receives a command and the session identifier, and forwards the command to the nano sandbox based on the session identifier. The MCP process executes the command within the nano sandbox, and responds over the SSE channel to the MCP client. When the SSE connection closes the nano sandbox can then be destroyed.