Abstract: Conventional testing of artificial intelligence (AI) agents is time-consuming and insufficient. Accordingly, embodiments provide an automated testing and validation framework for the tools utilized by AI agents. The framework may leverage artificial intelligence, including a classifier for classifying tools, a generative model that creates test cases based on the tool classification, an optimization model that maximizes test coverage while minimizing computation cost, an edge-detection model that identifies edge test cases, and/or an execution engine that manages execution of the test suite. The framework may be integrated into a continuous integration and continuous deployment (CI/CI) pipeline.

Abstract: Typically, in order to preserve their specializations, AI agents operate with isolated knowledge bases to learn skills independently from other AI agents. This results in a waste of computational resources and reduces the adaptability of the AI agents and overall system. Accordingly, disclosed embodiments enable AI agents to learn from each other, while retaining their specialized capabilities. A knowledge manager may identify knowledge elements, within a knowledge repository, that are compatible with a receiving AI agent and do not diminish the specialization of the receiving AI agent. An incremental transfer process may be executed, with performance monitoring and rollback capabilities, to incorporate the knowledge elements into the receiving AI agent, while ensuring that the knowledge transfer does not diminish the specialization of the receiving AI agent. A cross-agent collaboration interface is also disclosed for knowledge sharing and collaboration between AI agents.

Abstract: State-of-the-art platforms for the development of artificial intelligence (AI) agents typically require users to manually define and configure each component of an AI agent. This results in a high barrier of entry for new AI agents, a high likelihood of errors, long development cycles, and inefficiencies, especially for non-technical users. Accordingly, embodiments use artificial intelligence to generate a complete specification of an AI agent based on a user's intent, for example, as expressed in natural language. In an embodiment, the intent is converted into a structured intent, from which one or more tasks are determined. Next, one or more tools are identified for each task, and one or more guardrails are generated for the AI agent. The user may modify the AI-agent specification, as needed or desired, via an intuitive interface, before the AI agent is generated and deployed.

Abstract: Methods, devices and systems for digitizing a patient's arch and manipulating the digital dental arch model. In one variation the methods includes producing a physical arch model for the patient's arch, separating the physical arch model into a plurality of arch model components, mounting the arch model components on a scan plate, capturing one or more images of the arch model components, and developing digital representations of the arch model components using the captured one or more images.