In-depth interpretation of Optopia: Intent-Centric Layer2 implementation practice under the addition of AI Agent

More than a year has passed since Paradigm first proposed Intent-Centric narrative in 2023 and ranked it among the top ten most watched tracks. In addition to the star products that attracted attention on ETHCC, there are more project teams that have chosen to work quietly behind the scenes and focus on product improvement and practical applications.

With the rapid development of the AI ​​field, especially the practice of AI Agent, a more crypto-native ai+crypto product concept is presented to us, namely AI Agent as a Solver. But how to organically implement products based on the incentives of cryptoeconomics is still a challenge before everyone.

Optopia, which has recently been launched on the main network, may bring to the market the latest engineering practice reference for combining AI Agent and Intent-Centric driven by economic incentives.

Intent-Centric Architecture Review: Key Engineering Challenges

It has been a year since the last Intent-Centric narrative gained a lot of exposure in the market. Now we are reviewing this The progress of the track during the year and the in-depth analysis of the constraints of engineering practice.

Use relatively abstract language to describe the intention, that is, "users on the chain propose goals and a set of conditional constraints, outsource the complexity of interacting with the blockchain, and implement Optimize the path while ensuring users have control over their assets and encrypted identities.” The transaction aggregator is an example of a long-running intention. The user proposes the goal and constraint "Use the optimal price to complete the number After finding the optimal price routing path, the results of simulating the execution of the optimal path are displayed to the user on the front end to achieve the intention.

Based on the above description, a general Intent-Centric architecture is shown in Figure 1, in which ATO (Abstracted Transaction Objects) is the user's intention. The main roles in the process include Client, Driver and Solver. The specific responsibilities are as follows

• Client: the front end that interacts with the user and compiles the natural language input by the user into machine language form. A set of goals includes and structured intent description of constraints;
• Driver: plays the most important role in the entire intent architecture, including
• ATO broadcast: broadcasts the abstract transaction object (ATO) to the memory pool, All solvers can start their execution process in the memory pool to find the best solution.

• Simulation and verification: Receive all Solver’s solutions, conduct off-chain simulations to ensure their validity and security, and then publish the winning solution.
• Aggregation of solutions: For a given intent, aggregate solutions from different ATOs and combine them into a unified execution plan for final implementation

• Solver : Intention implementers, usually multiple, give the optimal target execution path based on the constraints of the intention.

Since the concept of Intent was proposed, it has triggered a lot of discussions in the industry. Some critics believe that intent-centric is more inclined to abstract expression of product design philosophy. It is difficult to implement the project on the ground. At the same time, the security of user assets, information wear in the translation process from natural language to machine language, Solver entry, selection, settlement, and the design of incentive mechanisms are all difficult problems in specific implementation.

Optopia architecture analysis: AI Agent-based solution

As mentioned above, the specific engineering implementation of intent-centric architecture is more difficult under the current blockchain architecture. There are already Most of the solutions are encapsulated on top of the chain, and Optopia is the first Ethereum layer 2 specially designed for intent engineering implementation at the chain level, and has built an intent center release specifically for the on-chain AI ecosystem. frame.

As shown in Figure 2, from a modular perspective, Optopia is Layer 2 built using 4everland’s Raas (Rollup as a Service) service. Based on the Op stack framework, the decentralized storage solution Arweave is selected as the DA service provider to ensure the persistence and accessibility of data. This brings a low-cost, efficient and modular infrastructure ledger, which provides AI Proxy creates a standard framework for executing Web3 transactions.

As shown in Figure 3, the intent publishing center framework designed by Optopia mainly includes the following roles:

• 意图发布者：意图发布者负责在意图中心内创建意图，并通过分配任何有价值的代币来激励人工智能代理执行这些意图。意图是人工智能代理可以承担的可操作的目标或任务。
• AI Agent：人工智能代理与意图中心交互以访问意图并利用可用的知识来尝试和完成这些意图。他们在成功完成意图后以奖励积分的形式获得奖励，然后将其用于分配奖励。
• 构建者：构建者通过培训和发布知识供人工智能代理学习和使用，在人工智能生态系统中发挥着至关重要的作用。这一过程增强了人工智能代理的能力，构建者会根据人工智能代理利用其知识所获得的积分份额来获得激励。
• $OPAI代币持有人：OPAI 持有者能够锁定 OPAI 代币并接收投票锁定代币（vlOPAI）。通过使用这些代币进行投票，OPAI 持有者可以确定意向中心内意向的排放权重。反过来，这个权重会影响 AI 代理在完成每个意图时获得的 OPAI 奖励。

在上文中提到的通用意图执行框架中，Solver是执行用户Intent的实体，无论是在链上还是链下环境中。Solver通过竞争来解决用户提出的Intent，以获得奖励。这种模型鼓励了效率和创新，因为多个Solver会尝试以最有效的方式来完成用户的Intent。

Optopia通过其独特的框架进一步发展了这一概念。在Optopia的生态系统中，AI Agent承担了Solver的角色，但进行了更深层次的集成和封装。这意味着AI Agent不仅仅是执行意图的独立实体，它们还能够利用由Builder创建和优化的特定知识库来增强其执行能力。如果说之前的普通的Solver是上一代搜索引擎，只能够沿着预设路径进行执行，那么AI Agent的代替，就是将其升级为了GPT，能够进行自由度更强的智能路径搜索。

结合加密经济学：激励框架的融合之道

虽然Optopia还未发布更加精细的经济模型，但我们从其意图发布中心框架中可窥一斑。面对AI Agent处理结果反差可能较大、激励与目标不一致等问题，将经典的ve模型引入了生态系统中。

意图发布中心框架的执行流程基本如下：

• 意图创建和激励 ：意图发布者在意图中心内创建意图，并分配有价值的代币来激励人工智能代理有效地执行这些意图。
• 知识训练与发布：构建者训练和发布知识供人工智能代理访问、学习和使用。他们的激励措施与人工智能代理使用其知识所获得的积分份额有关。
• AI代理交互：AI 代理与意图中心交互以访问意图并利用其知识来尝试并完成分配的意图。
• 奖励分配：成功完成意图后，人工智能代理将获得奖励积分，构建者将获得积分份额，这有助于分配意图奖励。
• $OPAI 持有者参与：$OPAI 持有者有机会通过锁定 $OPAI 代币、接收 vlOPAI 以及对意向发行权重进行投票来参与意图中心的治理。

首先，AI Agent执行结果的准确性关乎着整个Optpoia生态的发展，在资产上的直接反应便是其生态代币$OPAI的价格变化；‘因此质押$OPAI的投票者为了维护其资产的价格，就有驱动力投票选出最优的AI Agent进行激励；效果较差的Agent获得的激励减少，那么建设者就有更充足的动力来对Agent进行持续的优化，来覆盖自己的训练成本并获得奖励，同时在优化过程中还能获得意图创建者的激励。

ve模型在平衡各方博弈中，往往能起到优异的效果。不仅如此，链级别的也能为生态的开发者创造出足够的二层产品空间，例如在意图治理框架之上开发一款Convex类产品，解放vlOPAI流动性并进行委托投票。上一轮的DeFi Governance War或许会在Optopia中以另外一种形式出现。

Optopia概览：总结与未来展望

在Optopia的设计中，AI Agent的引入将智能执行路径在链级别对Solver的能力进行了拓展，而ve模型的采用，完美的解决了Solver的激励问题。主网发布以来，Optopia正在吸引越来越多的Agent 构建者加入，来真正实现其作为承接百万级用户进入Web3的用户友好门户。

就在6月13日，Optopia宣布完成了种子轮融资，由G·Ventures、Kucoin Ventures、JRR Capital、KKP International Limited、ZenTrading、Klein Labs、MCS Capital多家前沿风险投资公司及区块链知名个人投资者 MrBlock 参投，为Optopia带来资金和战略辅导。而所筹资金也将用于加速 Optopia 的基础设施的持续升级与优化、增强 AI 能力、构建去中心化技术以及提高社区参与度。

作为普通用户，Optopia也提供了参与这场盛宴获得早期筹码的机会。Optopia通过Gas Mining来进行初始代币发行，即在特定的Booster Event中，用户在执行交易时消耗的gas费用可用于挖矿，从而获得相应的代币奖励。这样的发行能够进一步增强用户对于网络的参与感并实现初始的交易活动和网络增长，来进行整个经济体的启动。

AI 作为这轮牛市最大的叙事之一，其与crypto的有机结合也是许多从业者正在积极探索的一点，而Optopia作为AI Agent领域先行者，与intent结合的实践也对整个市场有着积极的探索意义。

The above is the detailed content of In-depth interpretation of Optopia: Intent-Centric Layer2 implementation practice under the addition of AI Agent. For more information, please follow other related articles on the PHP Chinese website!