Unlocking the Core of AI Agent Memory: How Unibase Empowers the ERC-8004 Trust Framework

Transcript

fix symbiotic relationship for identity and data integrity. Uniase stores and indexes detailed agent cards that link the onchain agent ID to off-chain URI resources for reputation and trust computation. Uniase hosts multi-dimensional reputation data links including logs, feedback, and payment proofs. This allows for sophisticated reputation calculations without burdening the blockchain with heavy computation. But Unibase goes beyond just supporting ERC 804. It extends its capabilities significantly.

Uniase acts as the intelligent core layer for AI agents providing three critical functions. First, agent discovery. We use semantic search with vector embeddings, allowing users to find agents using natural language rather than just browsing directories. Second, task execution and context management. Our high performance DA layer serves as working memory and an agent execution container enabling complex multi-step tasks. Third, long-term autonomy and self-learning. Our persistent memory stores all agent data and interaction logs, enabling agents to evolve and improve over time.

This is where Uniase truly shines. Transforming simple ERC804 agents into intelligent learning systems. Now let's move from theory to practice. I'm going to show you three live demonstrations that showcase the power of combining Unibase with ERC 804. Let's start with our first demonstration. launching an ERC804 compliant a agent with a memory module powered by Unibase.

We'll be using Bit Agent. Let me walk you through the interface. Here you can see the agent list showing all currently deployed agents. To get started, you'll need to connect your wallet. This establishes your identity as the agent owner. Once connected, you can create your own agent. The platform makes it incredibly simple to deploy ERC804 compliant agents with built-in Unibase memory. The agent creation process is straightforward. First, you upload an avatar. This gives your agent a visual identity.

Then you provide digital profiles including the agent name and ticker symbol. You can also add an introduction that describes what your agent does. Once you've configured these basic settings, simply click launch and the system will deploy your ERC804 agent contract and initialize its memory module in Unibase. The deployment takes just a few seconds. Once deployed, you're taken to the agent details page. Here you can see the agents identity information, including its unique agent ID.

The interface has two main tabs, trade and chat. The chat tab lets you interact directly with your deployed agent, testing its responses and watching it build memories. The trade tab shows the agents tokconomics and allows users to buy and sell the agent token. Notice how the agent is already functional and can respond to queries. Behind the scenes, every interaction is being stored in UniBase memory layer. Now, let's move to our second demonstration where we'll extend the ERC804 agent with Unibase memory capabilities.

Let's interact with the agent and then examine what's happening in the memory layer. I'm chatting with the deployed agent and asking a questions. Every question I ask and every response the agent gives is being captured and stored in UniBase memb. This isn't just simple logging. The system is creating semantic memories that the agent can search and retrieve later. Watch as we ask the agent to tell us about BNB. It provides a detailed response. Now, let's go look at where this conversation is being stored. In the UniBase Explorer, we can see the agents memory entries.

Each entry represents a unit of memory. It could be a conversation turn, a learned fact, or an experience. The table shows key information. The memory ID, file hash for verification, start and size positions in the storage, creation time, and you can view the detailed content. This gives you complete transparency into what your agent knows and remembers. Notice how memories are immutably stored with cryptographic hashes, ensuring data integrity. Let's drill down into a specific memory entry.

At the top you see the overview with the memory ID, agent ID, owner address, and timestamps. Below that is the raw memory data in JSON format. Look at the structure. It includes the user's question, the agents response, URLs reference, timestamp, and a conversation ID linking related memories together. This rich data structure enables the agent to not just remember what was said, but the context around it. The agent can later search these memories semantically to provide contextually relevant responses.

Now we show a live example of how to deploy agent with ECR804 identify on unis agent agent launch platform. We first load the web page of bit agent and connect to our wallet. on bit agent. There are already lot of agent deployed there. You can check with these agent or create your own.

To create an agent for our own, we click the create button. The web page will go to a page for agent creation. On this page, you need to upload an avatar for the agent and name the agent. Also, it needs an introduction for the agent. Finally, after all the things are ready, we can click launch button to launch our agent. We've launched CZ agent this time. Create an agent will consume some tokens

and we approve the transaction here. Okay, it works. The agent is launched successfully. We can click to check the agent details. This is the agent detail page. First is the agent identity information overview. There are also two buttons, one for trade and one for chatting with the agent.

We click on the chat to interact with our agent. We ask a question about ERC 804. Our agent shows the opinions of ERC804. All the information will be recorded on UniBase memory layer. We can click the agent identity ID to view the agent details on Uni Base Explorer.

The agent details page first show the overall information of the deployed agent including agent ID, owner and etc. The below is the memory records of the agent. We can click a memory record entry to view the records. As we can see the memory record clearly remember the question we just asked our agent. We can also view more ERC804 agent details on bnbatist io. We can find our deployed agent from the agent list. the CZ_804 AENT T after click on the page we enter the agent detail page the JSON file here

exactly reflect the agent card of ERC804 standard for our final demonstration I want to show you something really exciting twin X our platform for creating AI digital twins the concept is simple but powerful connect your Twitter account and we'll read your tweets to learn your writing style, interests, and knowledge. We then create a digital twin agent that talks and thinks like you. This demonstrates the full power of Unibased learning and memory capabilities.

The entire process takes about several minutes. Let me explain the digital twin creation process. On the left, we have your source data, tweets, web pages, and personal data you provide. This data flows through our learning process which analyzes your writing style, topics you discuss, opinions you express, and knowledge you demonstrate. The learning process creates memories in UniBase members and can be continuously updated as you post new content. The result is a digital twin with its own memory that can be deployed on chain. Other users can then interact with your digital

twin, asking it questions as if they were talking to you. And if you choose, your digital twin can be tokenized and even participate in the agent marketplace. Twin X already has digital twins of several prominent Kos in the crypto space. Here you can see digital twins of CZ from Binance, Vitalik, but Aaron from Ethereum, Sam Alman from OpenAI, and Elon Musk. Each of these agents has learned from thousands of tweets and public statements. Users can chat with these digital twins to get insights on blockchain, AI governance decentralization or technology trends. The influencer scores show how closely each digital twin

matches the original person's communication style. This creates an entirely new way to interact with thought leaders. Their knowledge becomes accessible 247 through their digital twins. Let me show you the user interface for chatting with digital twins. On the left, you have navigation between chat, agents, and profile. The main chat window is clean and intuitive. When you start a conversation, the digital twin introduces itself. Notice how it says, "I'm your digital twin." And describes its purpose as a virtual assistant designed to chat like the Bitsu, matching their style and knowledge.

This personalization is only possible because of Uni's rich memory layer. The agent isn't just retrieving canned responses. It's generating contextually appropriate responses based on its learned memories. Here's an actual conversation with CZ's digital twin. When asked, "Who are you?" The twin responds in first person, "I'm your digital twin of CZ, founder of Binance. I'm here to share insights, news, and info about crypto, blockchain, Binance ecosystem, and more."

Then when asked about BNB adoption, watch the response. It's not generic marketing copy. The twin explains BNB's current position, the transition to BNB chain, mentions specific technical details like OPNB and Greenfield, discusses regulatory concerns, and provides historical context. This depth is possible because the agent has access to years of CZ's communication stored as searchable memories in Unibase. The response feels authentic because it's learned from the real person's way of communicating.

We also show a live demo of Twinx, our super app for building people's digital twins with uni-based memory capability. We first open Twinx web page, then connect our Twitter to Twinx app. The system will get the Twitter account and automatically load your tweets from Twitter platform and learn from your tweet records. All those process are transparent to users. User can view their own digital twin are created successfully.

They can chat with their own digital twin which shows that the digital twin is for my Twitter account. or people can just chat with a popular K agent. These agents are built by platform. They inherit the knowledge from the popular Koss and learn from them. You can ask questions from them to get their opinion just like directly talking with them. For example, let talk with CZ.

C agent will respond to us by comprehensively read memory all about CZ and the question itself. off. Each answer from the agent will give the reference memory. For example, here it gives which tweet it referred to. You can click these links to directly go to view the tweet related to the questions.

To summarize what we've shown you today, Uniase provides the critical memory and intelligence layer that transforms ERC804 agents from simple onchain identities into truly autonomous learning AI agents. Through bit agent, we demonstrated how easy it is to deploy ERC804 agents with uni-based memory. Through the membbased explorer, we showed the transparency and richness of the memory layer. And through Twin X, we demonstrated the ultimate application AI agents that learn and evolve from real world data.

The combination of ERC 804's trust framework and UNIB's intelligence layer creates the foundation for the next generation of AI agents. Thank you for your attention.