Private Personalized AI agent with XTrace

What is an AI agent?

An AI agent is an autonomous software entity designed to perform tasks by perceiving its environment, processing information, and taking actions to achieve specific goals. An AI agent typically comprises three core components:

1. Intelligence: The large language model (LLM) that drives the agent’s cognitive capabilities, enabling it to understand and generate human-like text. This component is usually guided by a system prompt that defines the agent’s goals and the constraints it must follow.
2. Knowledge: The domain-specific expertise and data that the agent leverages to make informed decisions and take action. Agents utilize this knowledge base as context, drawing on past experiences and relevant data to guide their choices.
3. Tools: A suite of specialized tools that extend the agent’s abilities, allowing it to efficiently handle a variety of tasks. These tools can include API calls, executable code, or other services that enable the agent to complete its assigned tasks.

What is RAG?

Retrieval-Augmented Generation (RAG) is an AI technique that enhances large language models (LLMs) by integrating relevant information from external knowledge bases. Through semantic similarity calculations, RAG retrieves document chunks from a vector database, where these documents are stored as vector representations. This process reduces the generation of factually incorrect content, significantly improving the reliability of LLM outputs.\cite{RAG}

A RAG system consists of two core components: the vector database and the retriever. The vector database holds document chunks in vector form, while the retriever calculates semantic similarity between these chunks and user queries. The more similar a chunk is to the query, the more relevant it is considered, and it is then included as context for the LLM. This setup allows RAG to dynamically update an LLM’s knowledge base without the need for retraining, effectively addressing knowledge gaps in the model’s training data.

The RAG pipeline operates by augmenting a user’s prompt with the most relevant retrieved text. The retriever fetches the necessary information from the vector database and injects it into the prompt, providing the LLM with additional context. This process not only enhances the accuracy and relevance of responses but also makes RAG a crucial technology in enabling AI agents to work with real-time data, making them more adaptable and effective in practical applications.

What is Agent Memory?

AI agents, by default, are designed to remember only the current workflow, with their memory typically constrained by a maximum token limit. This means they can retain context temporarily within a session, but once the session ends or the token limit is reached, the context is lost. Achieving long-term memory across workflows—and sometimes even across different users or organizations—requires a more sophisticated approach. This involves explicitly committing important information to memory and retrieving it when needed.

Agent Memory with blockchain:

XTrace  – A Secure AI Agent Knowledge & Memory Protocol for Collective Intelligence – will leverage blockchain as the permission and integrity layer for agent memory, ensuring that only the agent’s owner has access to stored knowledge. Blockchain is especially useful for this long persistent storage as XTrace provides commitment proof for the integrity of both the data layer and integrity of the retrieval process. The agent memory will be securely stored within XTrace’s privacy-preserving RAG framework, enabling privacy, portability and sharability. This approach provides several key use cases:

Stateful Decentralized Autonomous Agents:

• XTrace can act as a reliable data availability layer for autonomous agents operating within Trusted Execution Environments (TEEs). Even if a TEE instance goes offline or if users want to transfer knowledge acquired by the agents, they can seamlessly spawn new agents with the stored network, ensuring continuity and operational resilience.

XTrace Agent Collaborative Network:

• XTrace enables AI agents to access and inherit knowledge from other agents within the network, fostering seamless collaboration and eliminating redundant processing. This shared memory system allows agents to collectively improve decision-making and problem-solving capabilities without compromising data ownership or privacy.

XTrace Agent Sandbox Test:

• XTrace provides a secure sandbox environment for AI agent developers to safely test and deploy their agents. This sandbox acts as a honeypot to detect and mitigate prompt injection attacks before agents are deployed in real-world applications. Users can define AI guardrails within XTrace, such as restricting agents from mentioning competitor names, discussing political topics, or leaking sensitive key phrases. These guardrails can be enforced through smart contracts, allowing external parties to challenge the agents with potentially malicious prompts. If a prompt successfully bypasses the defined safeguards, the smart contract can trigger a bounty release, incentivizing adversarial testing. Unlike conventional approaches, XTrace agents retain memory of past attack attempts, enabling them to autonomously learn and adapt to new threats over time. Following the sandbox testing phase, agents carry forward a comprehensive memory of detected malicious prompts, enhancing their resilience against similar attacks in future deployments.

How to create a Personalized AI agent?

To create an AI agent with XTrace, there are three main steps to follow:

1. Define the Purpose: Determine the specific tasks and goals the agent will accomplish.
2. Choose the AI Model: Select a suitable LLM or other machine learning models that align with the agent’s requirements.
3. Gather and Structure Knowledge: Collect domain-specific data and organize it in a way that the agent can efficiently use.
4. Develop Tools and Integrations: Incorporate APIs, databases, or other services that the agent may need to interact with.

How to create a Private Personalized AI agent with XTrace?

XTrace can serve as the data connection layer between the user and the AI agents. Users will be able to securely share data from various apps into the system to create an AI agent that is aware of the user’s system. By leveraging XTrace’s encrypted storage and access control mechanisms, AI agents can be personalized without compromising user privacy. Key features include:

1. Seamless Data Integration: Aggregating data from multiple sources securely.
2. Granular Access Control: Ensuring only authorized AI agents can access specific data.
3. Privacy-Preserving Computation: Enabling AI agents to learn from user data without exposing it.
4. Automated Insights: Leveraging AI to provide personalized recommendations based on securely stored data.
5. User Ownership: Empowering users with full control over their data and how it is used.

How do we use XTrace private RAG for (L)Earn AI🕺?

1. We send learning materials in LLM friendly format to LNC RAG at XTrace
   
2. Once (L)Earn AI🕺 gets the question, first it talks to private RAG and retrieve relevant information
   
3. The LLM hosted at NEAR AI infrastructure generates a response based on both its pre-trained knowledge and the retrieved information!
   
4. Learners are encouraged to provide feedback and get 4nLEARNs to improve (L)Earn AI🕺 to work better for NEAR community!