Talus allows local design and deployment of decentralized on-chain intelligent agents, seamlessly, without trust, and operably utilizing on-chain and off-chain resources and services.
Written by: Shenchao TechFlow
The trend of AI Agents continues.
On Base and Solana, several protocols and memes related to AI Agents have emerged, stirring up market funds and attention.
However, the current AI Agent protocols are more application-layer focused, generally carving out their own AI tracks within existing public chain ecosystems;
However, major infrastructure projects have always been a higher valued narrative in the crypto world (whether the market buys it is another matter), will creating a chain specifically for AI Agents, allowing more AI Agents to operate on it, lead to a higher narrative ceiling?
Or conversely, in the current market environment where users are skeptical about VC coins, if they can ride the wave of AI Agents, could it become a lifeline for some infrastructure projects?
While you are still in doubt, someone has already started working on it.
VCs are flooding the battlefield, identifying AI agent projects
On November 26, the L1 blockchain Talus Network, designed specifically for AI Agents, announced that it raised 6 million USD in a round led by Polychain, with participation from Foresight Ventures, Animoca, Geek Cartel, Echo, and others.
At the same time, a group of angel investors such as Polygon co-founder Sandeep Nailwal, Sentient core contributor and Symbolic Capital co-founder Kenzi Wang, 0G Labs CEO Michael Heinrich, Allora Labs CEO Nick Emmons, and Nuffle Labs co-founder Atlan Tutar also participated.
As early as February of this year, when the narrative around AI agents was not this strong, the project completed a $3 million first round of financing, also led by Polychain Capital, with participation from dao5, Hash3, TRGC, WAGMI Ventures, and Inception Capital.
As a result, the total financing amount for Talus has now reached 9 million USD.
Interestingly, the 'L1 designed specifically for AI Agents' has caught the attention of another AI Agent.
Recently, the rapidly rising AI agent @aixbt_agent on Base has also keenly captured the Talus Network. Aixbt is an AI agent that monitors hot topics in crypto Twitter and can analyze and judge events happening in the industry.
Aixbt believes that Talus can build AI agents that operate entirely on-chain and claims to be focusing on this trend.
This wave of promotion undoubtedly also increased the popularity and discussion of Talus, and in an environment where AI memes are everywhere, a serious infrastructure project has attracted more attention.
Designing L1 for AI Agents, performing system-level optimizations
So, how does Talus Network create an L1 specifically designed for AI Agents?
Before discussing this matter, there is a more critical narrative question --- why do AI Agents need a dedicated chain?
The existing AI ecosystem faces three major pain points: unclear ownership, lack of transparency, and lack of permissionlessness.
Specifically, in the current centralized AI systems, the control of resources is concentrated in the hands of a few entities, and users lack a voice over their own data and computing power; the AI decision-making process is often a black box operation, lacking auditing and verification mechanisms; users also find it difficult to customize and adjust AI services according to their own needs.
Interestingly, platforms in different ecosystems, such as Virutals and vvaifu, can enable users to create AI agents themselves, but more efforts are being made in the direction of permissionless, followed by the tokenization of AI agents, sharing asset revenue through holding tokens.
The question of who owns this AI, whether it is actually an AI behind it, and other related questions still require some infrastructure to answer.
Thus, a public chain specifically designed for AI Agents can solve problems through classical blockchain solutions:
Ledger --- Clear records and transactions of resource ownership
Contract --- Transparency and verifiability of the decision-making process
Cryptography --- Permissionless open ecosystem
In terms of projects, Talus allows local design and deployment of decentralized on-chain intelligent agents, seamlessly, without trust, and operably utilizing on-chain and off-chain resources and services.
It has established a protocol that can represent, utilize, and trade these agents, resources, and services in a permissionless and verifiable manner.
Deconstructing the design of Talus, the following four levels of technical component combinations are worth noting:
Infrastructure Layer: A combination of Cosmos SDK and CometBFT
The Cosmos SDK is already relatively mature and reliable, but more importantly, its modular features allow the entire blockchain system to be flexibly expanded like building blocks. When AI technology iterates quickly, this flexibility becomes particularly important.
Contract Layer: The Move language makes design elegant
The native object model of the Move language makes on-chain management of AI resources natural and elegant. For example, an AI model in Move can be directly represented as an object with clear ownership and lifecycle, which is much simpler than traditional account-based blockchain models. Additionally, the concurrent processing capability of MoveVM can support hundreds or thousands of AI Agents running simultaneously, which is unimaginable in traditional serial execution environments.
Resource Mapping Layer: Mirror Objects System
This system cleverly solves the problem of how AI resources are represented and traded on-chain. When you need to use a large language model, you cannot place the entire model on the chain.
To explain in simple terms, you can think of Mirror Objects as the "digital avatars" of these off-chain resources, through which on-chain smart contracts can reliably operate off-chain AI resources.
Specifically, the Model Object is responsible for the on-chain representation of AI models, which not only records the model's metadata but also includes access permissions and usage conditions for the model. The Data Object manages access control for datasets, ensuring privacy and security of data when used by AI models. The Computation Object tokenizes computing resources, allowing computing power to be traded freely on-chain like cryptocurrency.
Verification Layer: Multi-layer verification solutions
For ordinary AI Agent interactions, such as conversations with chatbots, lightweight digital signatures can be used to ensure the authenticity of responses.
In high-risk scenarios like financial decision-making, zero-knowledge proofs can be enabled to ensure the correctness of the decision-making process without disclosing specific details.
For scenarios that require quick responses but can accept delayed verification, such as AI NPC behavior in games, an optimistic fraud proof mechanism can be used to ensure final correctness while maintaining performance.
For more technical details, you can refer to our previous articles: (Interpreting the Talus White Paper: Decentralized AI Agent Center)
Infrastructure at the back, AI dating applications at the front
Currently, Talus itself is still in the testnet phase, and it will take time to go live on the mainnet.
From the perspective of project operation and attracting user attention, while holding back major infrastructure initiatives, periodically releasing some applications as pilot projects can allow the market to see the usability of this L1 while increasing confidence.
At the same time as the financing news was announced, Talus also announced its first application in the ecosystem, "AI Bae". The "Bae" in this name comes from the internet slang "Before Anyone Else", suggesting the social attribute of this application.
Interestingly, Talus chooses to position its first application as an AI dating game rather than a more serious financial or business application, making its intention to attract more ordinary users through interesting applications clearer.
From the current information disclosed, AI Bae will support users in creating and customizing their own AI partners and introduce a Polymarket-style betting mechanism. This design is quite creative: it not only allows users to interact with AI but also enables them to tokenize their AI partners, turning them into exclusive memecoins. In other words, your "digital boyfriend/girlfriend" can not only chat with you but may also become an asset with market value.
This approach of mixing social, gaming, and financial elements is not uncommon in the crypto market. For these new public chains seeking to break through, reasonably utilizing popular gameplay might also be an effective way to find a breakthrough.
Currently, AI Bae has opened whitelist registration. In the current crypto market, which generally looks down on new public chains and infrastructure, Talus's unique approach may bring some unexpected surprises to the project. After all, in a bull market, sometimes an interesting application is much more effective than empty talks about technical advantages.
Task gameplay, old flavor
In addition to the dating application mentioned above, Talus has also launched a gamified task activity --- "Enchanted Seasons". The first season activity is named "The Awakened Orb", running from November 11 to January 11 next year.
This design somewhat resembles "playing a game": daily tasks, weekly quests, team challenges --- from the perspective of the task system, it is indeed a common operational method in Web3 projects. Currently, users can participate in tasks such as binding social media and posting to earn points, which is also a familiar old flavor from previous projects.
However, in the current market environment, user enthusiasm for pure task systems has diminished significantly, and how to further design more differentiated tasks or clarify the economic value that points can bring may become the key to breaking through.
Even for an L1 specifically designed for AI Agents, initial operations still cannot do without traditional community incentive models.
In the crypto world, no matter how advanced the technology is, it needs to operate narratives and assets from the perspective of user psychology and behavior patterns to achieve success.