Talus: The Missing Infrastructure for the Autonomous Digital Economy

Key Takeaways

• In the AI-driven digital era, productivity and creativity hinge on how effectively diverse AI tools are coordinated.

• The same holds true for the on-chain ecosystem, where countless AI agents are expected to interact at scale around digitized real value, driving a new economy and underscoring the importance of AI-based workflows.

• To this end, Talus introduces Nexus—a next-generation, fully composable, high-performance decentralized AI agent framework that leverages Sui as its coordination layer, establishing itself as core infrastructure for an autonomous Web3 economy.

1. Introduction

Civilization has advanced by continually abstracting complexity to create simpler, more intuitive, and more convenient ways to interact with the world. Today, in the digital age where most interactions occur over internet-based networks, this evolutionary trajectory is increasingly led by AI. More and more technology companies are leveraging AI to automate complex foundational tasks and deliver sophisticated use cases—expanding the reach of AI into our daily lives and broadening the scope of human activity.

In this context, what becomes ever more important is how we deploy and orchestrate these diverse AI tools. Users and builders alike are beginning to combine not only traditional centralized AI systems but also purpose-built AI agents tailored to their own unique contexts to explore smarter ways to leverage AI technology. By weaving together specialized agents for highly segmented tasks into cohesive flows, they are enabling broader and more refined decision-making—ultimately maximizing productivity and creativity.

This growing interest in AI-powered workflows is also emerging within the onchain ecosystem. At its core, a smart contract platform represents a programmable substrate for digitizing and activating “value,” while offering the composability and flexibility needed to support a wide range of use cases within digital networks. From this perspective, it's hard not to imagine a future where countless AI agents interact with “real value” in onchain environments—unlocking new economies, expanding industrial frontiers, and enabling entirely novel applications.

In particular, most AI systems today—especially centralized ones—remain confined within closed, centralized environments, making it impossible to audit their logic or coordinate and trust interactions at scale. Therefore, to fully unlock AI’s potential in an open digital economy, we need a new kind of onchain infrastructure—one that enables agents to reason, act, and collaborate with complete transparency.

This article explores the architecture of Talus, a decentralized platform for deploying, coordinating, and monetizing autonomous systems through onchain workflows for AI agents - Talus is a new onchain protocol designed to serve as the foundation for a wide range of AI agents and agent-based applications. We look at how Talus enables AI agents—functioning as interfaces for AI capabilities—to operate within blockchain environments, and how such a workflow platform can dramatically enhance the usability and versatility of agents. We also examine why Talus chose Sui as its coordination layer and explore the technical robustness required to enable a new class of agents with more advanced capabilities, while supporting a scalable, interoperable agent ecosystem.

2. The Transformative Potential of Onchain AI Agents for a New Paradigm

2.1 Abstract Away the “How” in Onchain Context

Blockchain technology has opened up the potential to expand the economies of various industries by enabling the full projection of “value” in the digital realm. However, from a practical standpoint, it is still difficult to say that the technology has matured enough to comprehensively support a wide range of use cases. This is not only due to persistent scalability challenges in reliably processing large-scale transactions in decentralized environments, but also because the underlying infrastructure for precisely representing digital value and enabling seamless interaction between assets remains incomplete.

In this context, abstraction technologies—such as Account Abstraction and Intents—have gained significant attention for their ability to simplify user experiences by shielding users from the technical complexity of protocol-level operations. Now, building on this wave of abstraction, a new kind of interface is emerging: onchain AI agent solutions that integrate a wide array of technologies to operate around users’ goals, serving as an entirely new interaction paradigm.

Onchain + AI agents go beyond the role of simple automation tools offered by traditional AI agents, and are instead designed as fully fledged participants in the digital economy that exist onchain. Each agent holds a unique onchain identity, can own and transfer assets under delegated authority from users, execute smart contracts and even evolve its behavior over time based on verified outcomes. This enables them to automate a wide range of Web3 interactions—such as portfolio management, in-game store operations, and DAO governance participation—while flexibly expanding their use cases.

Moreover, leveraging the inherent transparency, openness, and interoperability of blockchain, agents can freely explore and reason across various protocols and services, while coordinating interactions and organically collaborating with other agents specialized in different functions. For example, a user could simply state a goal in natural language—such as “maximize returns this month with 3 ETH”—and the AI agent would automatically coordinate the necessary actions, from swaps and lending to staking, even negotiating with other interacting agents when needed.

In short, what once felt like a technically intimidating onchain environment can now be approached with nothing more than a clear intent—users simply specify what they want, and agents handle the how in an efficient and transparent way. This paves the way for a new kind of experience, rooted in real digital value, that was simply not possible in the Web2 paradigm.

2.2 Brief History of Onchain AI Agent Landscape

It’s been some time since ChatGPT took the world by storm, but the onchain AI agent space only recently entered the spotlight—most notably in late last year, when Marc Andreessen, co-founder of a16z, was revealed to have sent approximately $50,000 worth of Bitcoin into a project called $GOAT (Goatseus Maximus)*. This event marked a turning point, pushing the concept of AI agents into mainstream crypto discourse.

Looking back on its short history - most of the early AI agent-related tokens that followed $GOAT initially rode the wave of internet meme culture, successfully triggering bursts of speculative demand in a short span of time. However, these early tokens quickly revealed clear functional limitations as meme tokens. As a result, market expectations for utility began to rise, and subsequent tokens started incorporating features such as community participation, real-time social interactivity, and more sophisticated decision-support capabilities to enhance real-world usability.

A notable example is the Virtuals Protocol’s $LUNA, an AI-powered virtual influencer and autonomous character that interacted with users across TikTok, X, and Telegram, offering a new kind of social experience. Tokens like $AIXBT and $ZEREBRO evolved into agents that support decision-making through integrations with DeFi, blockchain tools, and creative platforms. Meanwhile, $AI16Z and $EMP pursued technical advancement—such as cross-chain operability, dApp integration, and hardware compatibility—while launching initiatives aimed at building sustainable ecosystems. $GRIFFAIN enabled crypto payments and purchases through simple natural language inputs, while $COOKIE positioned itself as a core component of the crypto economy for InfoFi, combining data analytics with community governance.

Beyond these, AI agents specialized for specific purposes—such as smart contract auditing and abnormal transaction detection—are rapidly increasing in number. In the DeFi sector in particular, the continued maturation of the ecosystem is enabling these agents to integrate with financial applications, making their utility ever more sophisticated (e.g., DeFAI). Furthermore, as the need for deep infrastructure to support truly onchain-identity-driven autonomous AI agents grows, decentralized onchain frameworks and workflow platforms are emerging in quick succession.

In short, AI agent projects in onchain space are evolving rapidly from speculative assets into purposeful, functional digital agents that are carving out distinct and valuable positions in the market.

*$GOAT is an AI-powered memecoin launched on the Solana blockchain. It gained traction through viral Twitter (X) content led by an AI bot named Truth Terminal, built on Llama-70B.

2.3 Infrastructure Bottlenecks Limiting Scalable AI Agent Use

Yet, despite the growing emergence of increasingly specialized and granular onchain AI agents, we still face significant challenges in seamlessly utilizing them, combining them into coherent workflows, and operating them in a truly intelligent manner. This is largely because most existing onchain environments face technical & operational limitations that prevent them from delivering a smooth and intuitive user experience.

For instance, users are often required to manually visit multiple applications and sign complex onchain transactions themselves through their non-custodial wallets, and sometimes paying substantial fees in various assets. Moreover, differing standards across ecosystems frequently force users to make tedious asset conversions or interact through indirect and inefficient routes.

These limitations pose hurdles not only for human users, but also for onchain AI agents, making it difficult for them to construct seamless workflows onchain. Therefore, for existing and future onchain AI agents to fully perform their intended roles and collaborate effectively with one another, their underlying onchain workflows must be supported by a foundational infrastructure that meets essential standards for functionality, performance, and security—ensuring smooth, reliable execution.

To break this down further, we can identify three core requirements for the next-generation infrastructure that will enable a scalable AI agent economy:

1. High Scalability — the ability to handle a large volume of transactions initiated and mediated by agents in a reliable and cost-efficient manner.

2. A Flexible Technical Stack and Modules that enables the dynamic structuring of assets and interactions, while allowing complex logic to be expressed through a unified standard.

3. Multichain Interoperability — allowing agents to explore across diverse ecosystems, unlock broader functionality, and seamlessly interact without being confined to a single chain.

Well-designed software is undoubtedly capable of processing interactions far faster and more accurately than any human. Yet ironically, such efficiency can sometimes introduce new layers of operational inefficiency. To effectively delegate a high volume of interactions to AI agents, the supporting infrastructure must be capable of handling large-scale transactions with both speed and affordability.

Furthermore, because most onchain activities by AI agents inherently depend on the onchain execution environment, a certain level of standardized abstraction—covering both asset structures and interaction logic—is critical for smooth operations. To enable richer and more sophisticated interactions, the technology stack should be able to flexibly define various forms of integrated standards. Additionally, stability modules such as permission control must be seamlessly implemented to ensure secure asset management without adding friction.

Lastly, given that each blockchain ecosystem has its own user base, culture, and landscape of synergistic projects, AI agents must not be siloed within a single chain. To unlock more expansive use cases, the infrastructure should be designed to flexibly support seamless multichain interoperability.

3. Talus' Decentralized Agentic Protocol Built with Sui Infrastructure

Talus is focused on building the infrastructure needed to overcome these challenges and create the highest-performing decentralized AI agents possible. To enable agents with new capabilities and ensure seamless interaction at the infrastructure level, Talus introduces Nexus—a fully composable, decentralized agent framework designed from the ground up. The goal is to establish Nexus as the foundational infrastructure for building an autonomous Web3 economy.

Unlike most onchain AI agent projects, which originate from EVM- or SVM-based ecosystems that already enjoy a mature environment with robust user bases and diverse project landscapes, Talus operates an independent infrastructure with its own orchestration node system, integrating Nexus with the Sui Blockchain as its coordination layer and leveraging Sui’s native Move language. Rather than creating just another agent project constrained by the limitations of an existing stack, this is an attempt to redefine the execution environment itself, enabling agents to operate with the same first-class capabilities as top-tier smart contracts or DeFi protocols, and do even more.

Though Move may not have the long-standing legacy of EVM or SVM, it offers a unique advantage in its high-performance transaction throughput and its object-centric architecture, which enables richer and more flexible definitions of assets and interactions. This makes it particularly well-suited for implementing complex business logic that would be difficult—or even impractical—to achieve in EVM or SVM environments, allowing such logic to be executed more easily and securely.

Talus recognized this potential early on. With the vision of enabling a new onchain AI economy—where autonomous AI agents can be owned by anyone, transparently managed, and collaboratively operated—Talus identifies Sui’s architecture as the most optimized infrastructure for realizing diverse AI agent use cases and facilitating seamless agent-to-agent collaboration. Based on this belief, it is building their agentic protocol, Nexus, with Sui. In the future, a range of additional modules—including marketplaces, staking mechanisms, and agent-native governance—will be introduced and integrated alongside Nexus.

To better understand what Talus is building, let’s first take a brief look at the core technologies behind Sui.

3.1 Why Sui: Only-Possible-on-Sui Features

3.1.1 Sui’s Move Language for Rich Representation

Sui’s Move language is a uniquely redesigned version of the original Move, reengineered by the Mysten Labs team to suit the Sui blockchain environment, drawing on their prior experience developing the original Move language during Meta’s Diem project. While it retains Move’s core philosophy of asset-centric design and strong safety guarantees, it introduces a unique architecture that balances performance, security, and expressiveness—all of which are essential in environments like AI agents, where complex asset states must be handled with delicacy.

At the core of Sui’s Move is the concept of a “Resource,” which defines assets as immutable units—ensuring that they cannot be duplicated or arbitrarily deleted. These assets are encapsulated within modules, enabling safe access through interfaces without requiring complex management from the outside, and allowing for fine-grained control through a robust permission system. With features such as a static type system, borrow checker, and formal verification, Sui Move enables developers to catch potential bugs and security risks before execution, enhancing reliability and trust.

Another key advantage of Sui is its adoption of an object-centric state model. This structure not only allows flexible representation of individual assets, but also captures relationships between them directly on-chain - the states of independent objects can be processed in parallel without bottlenecks, enabling both high throughput (TPS) and low transaction fees at the same time. Moreover, the ability for objects to own other objects is natively supported, enabling complex ownership structures—such as an agent owning another agent, or holding dynamic NFTs whose metadata evolves over time—to be expressed naturally and securely.

Lastly, Sui introduces a powerful feature called the Programmable Transaction Block (PTB), which allows up to 1,024 individual transactions to be bundled into a single atomic execution flow. This enables AI agents to handle complex conditional logic and multi-asset interactions as a single transaction, significantly improving both the efficiency and affordability of executing sophisticated workflows.

3.1.2 A Full-Stack Architecture and Developer-Friendly Modules for Seamless and Reliable Interactions

Beyond the advantages of the Move language, another compelling reason to build with Sui lies in its full-stack architecture—designed for fast and reliable transaction processing—and its extensive set of modules tailored to accommodate a wide range of business use cases.

From the outset, Sui has positioned itself as a high-performance blockchain infrastructure built for environments that demand rapid-fire, high-volume interactions—such as gaming, high-frequency trading (HFT), and real-time interactive services. To make this possible, Sui introduced a proprietary consensus engine called Mysticeti, which delivers sub-second finality. Mysticeti utilizes a DAG-based (Directed Acyclic Graph) structure and adopts an uncertified approach that skips certification rounds, significantly reducing consensus latency. By avoiding reliance on a single leader and instead leveraging the network’s total bandwidth, Sui allows multiple validators to process transactions simultaneously, reaching total-order consensus at remarkable speed.

To ensure stable support for ultra-fast processing and massive concurrency, Sui integrates a variety of technical innovations. For example, Remora enables horizontal scaling by distributing validator workloads across multiple machines, allowing TPS to increase linearly with hardware expansion. In addition, the use of Programmable P2P Tunnels enables on-chain aggregation and settlement of micro-transactions generated in applications like gaming and real-time trading, allowing for custom-tailored transaction design per use case. As a result, the network maintains high performance and avoids bottlenecks even during traffic surges.

On the security and reliability front, Sui’s architecture goes a step further. To defend against threats such as BGP hijacking, DDoS attacks, and DNS spoofing—common in general internet environments—Sui incorporates the SCION network architecture. This ensures consistent uptime even in latency-sensitive scenarios like large-scale online games. Moreover, with Walrus, Sui introduces an on-chain ㅇdecentralized storage layer capable of handling data from gigabytes to exabytes at cloud-scale. This is further secured through the SEAL protocol, which provides encryption and access control for stored data directly at the chain level.

Sui also offers a wide range of tools to enhance both user experience and developer accessibility. Features like Passkeys for social login and account recovery, GraphQL-based RPC 2.0, the Sui Move Prover, Bugdar, Move Registry, and various SDKs can form a robust foundation for integrating and scaling onchain AI agents that will be deployed through Talus. Additionally, native bridges support assets like Wrapped Bitcoin, Lightning Bitcoin (LBTC), Ethereum (ETH), Wrapped ETH (WETH), and Tether (USDT), while integrations with Wormhole and Axelar further expand Sui’s ability to interact flexibly with other chains.

By leveraging this full-stack architecture, Talus aims to lower the barriers to both UX and DX, positioning Sui as the Coordination & Value Layer and Walrus as the decentralized storage layer—together forming a sophisticated execution environment for the emerging AI agent economy.

3.2 Overview of Nexus: Talus’ Decentralized and Fully Composable Framework for Onchain Agents

3.2.1 Primer on Nexus

As illustrated below, Talus’s agent framework—Nexus—faithfully inherits Talus’ core design principles. Nexus consists of three key components that work together seamlessly: (1) the coordination and value layer powered by the Sui blockchain, (2) a decentralized data storage layer via Walrus, and (3) offchain execution nodes and tooling infrastructure.

Through this structure, Nexus aims to encapsulate offchain AI services into forms that an onchain virtual machine can understand, and define all services—whether executed onchain or not—in ways that are cryptographically verifiable via smart contracts. In other words, this modular design allows AI agents built on Nexus to operate onchain with the transparency and security of smart contracts, while also ensuring performance that meets practical demands. In this process, an agent’s workflow is transformed from a static script into a dynamic, verifiable DAG, allowing agents not only to execute tasks but also to understand context, reason across the entire workflow, collaborate with other agents, and continuously evolve.

Given that Sui serves as the coordination and value layer, the onchain components of Nexus are fundamentally built with Sui blockchain. This means that essential assets and tools used in composing agent workflows are either derived from existing Sui Move libraries/modules or newly defined using Sui Move. These agent workflows are fully hosted and executed within smart contract functions on the Sui blockchain.

Nexus defines three main categories of onchain components related to Talus agents:

• Nexus Onchain Packages (NOPs)

• Tool Packages

• Talus Agent Packages (TAPs)

A wide range of onchain services and tools—implemented through Tool Packages (using Sui Move)—are formally defined within various sub-packages of the Nexus Onchain Packages*. Builders who wish to develop their own agents can then compose and customize them using these predefined components**.

Some essential metadata required for composing agent workflows is stored in Walrus, the decentralized storage layer - to fully leverage onchain execution, the relevant metadata must be accessible onchain during runtime. However, storing such data directly on blockchains like Sui would be prohibitively expensive, which is why Talus instead opts to use a purpose-built, storage-optimized layer.

Walrus achieves cost-efficient scalability through full replication and an erase-and-share mechanism, while also offering high programmability by expressing data at the object level - for a more detailed explanation of Walrus, see Four Pillars' report.

*The tools defined through Tool Packages can include offchain AI utilities such as LLMs, image recognition or generation, text processing, mathematical computation, web access, and file handling. These tools are executed via Nexus’s offchain "Leader" system, where the Leader receives the result (or error) and relays it back into the onchain workflow.

**To ensure the reliability, security, and longevity of the tools that serve as agent primitives, Talus will offer economic rewards to tool providers while also requiring them to stake a certain amount of tokens—subject to slashing—to align incentives.

3.2.2 Workflow of Nexus

By leveraging the Nexus framework, developers can implement logic under a unified onchain interface while easily integrating offchain tooling via the offchain messenger. This enables a wide range of AI workflows to be invented and executed seamlessly across both onchain and offchain environments within a single, cohesive framework. Each agent’s workflow is recorded and settled onchain in a verifiable manner, allowing developers to customize trustworthy agents with confidence.

Source: Diagram showing Nexus V1 Interface flow | Talus Docs

To build an AI agent, developers use the provided Nexus Interface v1 to define custom logic that governs workflow execution, payment handling, and more. This process includes two key phases: workflow management and authorization for execution.

To get started, developers must first call the constructor function of the base Talus Agent Package to instantiate a new agent on the network - during this step, a unique agent ID is assigned and an authentication token is initialized to identify the package. Once deployed, the agent’s workflow can be defined in the form of a DAG.

The Talus Agent Package includes a built-in worksheet function to track the status of workflow execution. When a workflow is triggered, the agent creates a worksheet object to log its execution state, and sequentially initiates each tool in the DAG, beginning from the root node. As each step is completed, confirmation data is accumulated and, once all stages are verified to be successfully executed in order, the workflow is finalized*.

*Importantly, all gas fees and tool usage costs incurred during execution are automatically deducted from the pre-funded gas budget managed by Nexus. This design allows developers—especially those new to the ecosystem—to focus on designing workflow logic and assembling toolchains without needing to worry about payment infrastructure or transaction complexity.

From the user's perspective, interacting with a deployed AI agent only requires engaging with the onchain components. The overall process can be illustrated as shown above.

Much like a typical AI agent, once a user initiates interaction with an agent built through Nexus (i.e., Request Transaction), the onchain workflow module embedded in the Nexus package is triggered. This kicks off the execution of a predefined workflow in the form of a DAG onchain (i.e., DAGExecution)*.

During this process, the Nexus Leader Node system automatically handles the triggering and coordination of offchain tools. Acting as a type of messenger, the Leader serves as an oracle that intermediates communication with offchain infrastructure**.

*Each tool (represented as a vertex) in the sequential workflow can flexibly adopt its own safety level, but regardless of which tool is used, a result-based security model with strict validation can be applied. This ensures that the final output posted on-chain maintains proper integrity and consistency.

**The Leader system cannot directly access Talus agents or user assets. It is strictly limited to supporting the execution of onchain workflows within its predefined permissions and operates within a trusted execution environment (TEE).

3.3 Talus Agent Use Cases and Ecosystem Overview

In short, AI agents generated through Talus’ Nexus framework—each built as a distinct onchain object—are defined by three core principles: identity, onchain workflow, and autonomy. Put differently, Talus agents are not just chatbots or AI avatars; they are programmable digital economic actors, no different from any other user participating in onchain activity.

Accordingly, the broader ecosystem that Talus aims to build around these agents can consist of highly specialized Agent-as-a-Service (AaaS) entities, each designed to serve specific purposes across virtually every domain we’ve encountered so far—gaming, DeFi and financial services, scientific and data analysis, and product development, to name a few. These agents will be embedded into various services and protocols, autonomously performing purpose-specific tasks tailored to the needs of both end users and onchain applications, dramatically advancing the overall automation capabilities of the Talus ecosystem. Taking this one step further, we can even envision the emergence of a Agent Marketplaces (AM)—a marketplace where these sophisticated agents can be traded or extended with new functionalities.

As of now, while running its testnet, Talus is also actively forming partnerships with a wide range of players both within and beyond the Sui ecosystem, laying the groundwork for a vibrant agent economy by expanding the potential use cases of AI agents.

Below are some of the key ecosystem partners established so far, excluding Sui and Walrus.

Cubist

Cubist is a tooling provider for Web3 developers, offering a secure hardware-based key management solution called CubeSigner. Through integration with CubeSigner, Talus enables agents to maintain strong security with clearly defined access control, while also facilitating efficient and seamless cross-chain transactions.

SuiNS (Sui Name Service)

Sui Name Service serves as both an identity and interaction layer for the Sui network. Through the SuiNS interface, users can assign each Talus agent a ‘.sui’ domain to manage its onchain identity. In addition, agents will be able to leverage other interaction features provided by SuiNS—such as messaging and asset transfer functionalities.

Atoma Network

Atoma is a Sui-based, privacy-centric decentralized AI infrastructure that leverages a TEE-based node architecture to prevent data and model exposure during computation. By integrating Atoma’s verifiable AI inference technology, Talus aims to ensure that agent inference processes can be executed in a more transparent and trustworthy manner.

Marlin

Marlin is a verifiable computation layer that combines TEE with diverse computing resources to enable low-cost, scalable computation while safeguarding sensitive data. Through its partnership with Marlin, Talus will empower smart agents built within its framework to perform computations backed by verifiable proofs and attestations.

Swarm Network

Swarm Network is a blockchain-based multi-agent AI framework designed to enable collaborative problem-solving among multiple AI agents. By integrating the Nexus framework as a verifiable onchain execution layer into its agent stack, Swarm enhances both the transparency and scalability of agent orchestration and coordination.

Vana

Vana is a data sovereignty protocol built around "Vana Vaults," which allow users to securely store their data and control how it is used—while also proving their contributions and earning revenue in return. Talus leverages Vana’s extensive user-managed data network to access diverse datasets needed for training and fine-tuning AI agents, enabling more precise agent development.

NODO

NODO is a multichain, AI-powered yield layer supported by the Sui Hydropower Accelerator, focused on optimizing asset allocation and maximizing user returns through a suite of specialized AI agents. In partnership with Talus, NODO brings together agents dedicated to AI-driven analytics, spread optimization and MEV protection, and cross-protocol rebalancing. These agents are orchestrated in a coordinated manner, enabling users to dynamically and strategically allocate capital based on real-time data.

ZO

ZO is the first intelligence-driven perpetual DEX protocol built on Sui, offering zero-slippage trading, efficient price discovery, and sub-second transaction execution. Through its partnership with ZO, Talus is integrating a range of purpose-built AI agents into the platform, significantly enhancing trading accessibility and efficiency. Traders can leverage a variety of powerful decision-support tools—from market monitoring and personalized strategy recommendations to the automation of repetitive tasks.

4. Looking Ahead

As AI continues to prove its ability to make human life tangibly more convenient and sees rapid adoption across all sectors of society, our dependence on it is only accelerating. Research into discovering and collecting meaningful data, along with developing effective learning algorithms and techniques, is progressing at a rapid pace—while hardware performance is advancing just as impressively. In parallel, governments around the world are recognizing that AI adoption is an inevitable trend and are proactively establishing governance frameworks to accommodate it. As Sam Altman has noted, the convergence of three forces—abundant intelligence, abundant energy, and good governance—is creating a positive feedback loop driving the next wave of AI-powered productivity.

Yet despite this potential, Web2-based AI systems remain fundamentally centralized, which imposes inherent limitations when it comes to designing advanced agents that rely on sensitive data or real assets, or enabling complex cooperation among multiple agents. In contrast, agent platforms built within trust-minimized Web3 environments are poised to break through these structural constraints, offering far greater potential for expanding how AI agents can be utilized.

Talus, recognizing this momentum, has strategically leveraged Sui—a high-performance blockchain infrastructure—as a coordination layer to overcome the technical limitations of Web3 and introduce a new standard that balances usability, verifiability, flexibility, and composability. Through seamless integration of on-chain and off-chain tooling, a robust verification architecture, and rapid expansion of its partner ecosystem, Talus is laying the foundational infrastructure for the next generation of verifiable AI agents—and more broadly, for the autonomous digital economy itself. As onchain agents evolve from novelty to necessity, Talus will stand as the protocol layer that powers their deployment, coordination, and monetization at global scale. This can clearly serve as a bridge that effectively connects the mature technical stack of Web2 with the real-world value of Web3, enabling meaningful use cases across a wide range of industries.

Of course, while the Sui ecosystem on which Talus is built excels in technological innovation, it is true that it still has a relatively smaller user base and fewer applications compared to EVM or SVM ecosystems. However, because Nexus is designed to provide a stable foundation for a wide range of Web3 applications, the emergence of successful applications built on it could drive growing demand for Nexus—creating a powerful flywheel effect where that demand, in turn, fuels even greater success.