Use Case #9 Agentic AI

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Agentic AI
Agent AI, also known as intelligent agents, refers to autonomous entities that perceive their environment, reason about their goals, and take actions to achieve those goals. These agents can operate independently or collaboratively to perform tasks, make decisions, and interact with humans or other agents. These AI assistants integrate with devices or act as virtual companions, providing a range of personalized services.

Problems to Solve:
• Response Accuracy: Existing virtual assistants may struggle with accurately understanding and responding to user queries, leading to frustration and inefficiency.
• Personalization: Current virtual assistants often lack personalization, providing generic responses that may not meet individual user needs.
• Privacy Concerns: Users may be apprehensive about sharing sensitive information with virtual assistants due to concerns about data privacy and security.
• Integration Complexity: Integrating virtual assistants with various systems and platforms can be complex and time-consuming.
• Difficulty in controlling and understanding how your personal AI assistant is using your data.
• Lack of transparency in the decision-making process of your personal AI assistant.
• Difficulty in ensuring that your personal AI assistant is acting in your best interests.

Weaknesses in Current Practices:
• Limited Context Understanding: Virtual assistants often struggle to understand the context of a conversation, leading to inaccurate responses.
• Lack of Provenance: Current virtual assistants may not provide information about the source or credibility of the data they provide, undermining trust.
• Privacy Risks: Virtual assistants may store and analyze sensitive user data, raising concerns about privacy breaches and data misuse.
• Fragmented Ecosystem: Integration with third-party services and platforms may be challenging due to the fragmented nature of the virtual assistant ecosystem.

How C2PA+TSP Can Help:
• Provenance Information: C2PA can provide provenance information for certain content types, allowing users to verify the credibility and reliability of information provided by the virtual assistant.
• Secure and Confidential Communication: TSP supports authentic and optionally confidential distribution of content, ensuring that user interactions with the virtual assistant are secure and private.
• Decentralized Communication: Unlike typical web service models, TSP facilitates decentralized and distributed communication, enabling virtual assistants to interact seamlessly across boundaries without relying on centralized servers.
• Scalability: TSP's scalability ensures that virtual assistants can handle a large volume of user interactions efficiently, improving responsiveness and performance.

What Is Not in Scope:
• Natural Language Processing (NLP) Algorithms: Detailed specifications of the NLP algorithms used by the virtual assistant.
• User Interface Design: Design considerations for the user interface of the virtual assistant application.
• Integration with Specific Platforms: Detailed integration requirements with specific third-party services or platforms.
• Potential Bias: C2PA and TSP don't address potential biases within the AI model used by the assistant, which could influence health-related recommendations.

Additional Considerations:
the importance of responsible development and deployment of agentic AI systems, which includes personal AI assistants. This translates to ensuring:
Clear and Transparent Communication: The AI assistant should communicate its capabilities and limitations clearly, and how it uses your data.
User Control: You should have control over how the AI assistant interacts with your data and environment.
Accountability: There should be mechanisms in place to hold developers and service providers accountable for any misuse of your data or harm caused by the AI assistant.