In 2026, one of the most closely watched narratives in the crypto industry is the deep integration of artificial intelligence and decentralized finance. Over the past year, AI Agents have moved from proof of concept to real-world applications, taking on tasks in on-chain research, trade execution, and portfolio management that previously required manual input from users. DeFAI (DeFi + AI) is no longer just an abstract technological vision—it’s rapidly becoming a tangible force reshaping how users interact with on-chain finance.
Upgrading on-chain asset management tools is especially critical in this evolution. Traditional DeFi users often have to juggle between wallets, decentralized exchanges, data platforms, yield protocols, and cross-chain tools, making a complete on-chain investment a high-cost endeavor in terms of both learning and execution. As assets spread across multiple blockchains and protocols, the bottleneck of manually managing portfolios becomes increasingly apparent.
AI is changing this landscape. From automated strategy execution to intelligent risk management and data-driven asset allocation, AI Agents are shifting on-chain asset management from "manual operation" to "autonomous execution." This article explores these three dimensions, using platforms like Velvet as case studies to systematically analyze how the convergence of AI and DeFi is upgrading the investment experience—and the real-world challenges this trend faces.
The Convergence of AI Agents and DeFi: From Concept to Infrastructure
To understand how AI is upgrading on-chain asset management, it’s essential to clarify the technical foundation of DeFAI. At its core, DeFAI brings AI Agents into on-chain financial scenarios, enabling them to perceive market conditions, autonomously formulate strategies, and directly execute on-chain actions. Unlike traditional trading bots that rely on fixed rules, AI Agents can dynamically adjust strategies in response to market changes.
In 2026, numerous research and venture capital institutions have dubbed this year the inaugural year of the "Agent Economy." AI Agents are no longer just chatbots or coding assistants—they are becoming independent economic actors, autonomously executing trades, optimizing yields, and managing assets on-chain. According to a DWF Ventures report, automation and AI Agent activity now account for about 19% of all on-chain activity, with over 17,000 AI Agents launched since 2025.
Velvet stands out as a representative project in this trend. As a DeFAI infrastructure platform, Velvet simplifies user participation in complex on-chain trading and portfolio management through AI Agents, intent-driven execution, and on-chain asset management tools. The platform currently supports major blockchains such as BNB Chain, Ethereum, Base, Solana, and Sonic, and has introduced a multi-agent AI co-pilot system. Users can conduct project research, asset analysis, trade execution, and portfolio management using natural language. Official data shows the platform has attracted over 100,000 users and created more than 10,000 strategy vaults.
The market performance of the VELVET token also reflects the growing attention to the DeFAI sector. As of July 15, 2026, according to Gate market data, VELVET is priced at $0.56570 with a market cap of $140 million, ranking 233rd. The price dropped 6.68% in the past 24 hours, but has risen 51.52% over the past 7 days, 74.58% over the past 30 days, and an impressive 965.38% over the past year. Its all-time high is $2.15464, and its all-time low is $0.04330. These figures indicate that, as a flagship asset in the DeFAI sector, VELVET has undergone significant price discovery and continues to attract strong market interest for its technological narrative and real-world adoption.
Automated Strategy Execution: Seamlessly Translating Intent into On-Chain Actions
The first key upgrade in on-chain asset management is the automation of strategy execution. Traditional DeFi trading requires users to specify every detail—selecting trading pairs, determining routing, confirming fees, and setting slippage parameters. When strategies involve multiple protocols and cross-chain operations, execution complexity grows exponentially.
Intent-driven execution models are changing this paradigm. Take Velvet as an example: users simply state their end goal—such as "convert assets to a specific token" or "build a particular asset allocation"—and the system automatically finds the optimal execution path. The process involves the user submitting their intent, an AI Agent parsing the requirement and generating executable on-chain actions, a Solver network analyzing liquidity sources to find the best execution, and finally, the aggregator and smart routing system completing the trade.
The core value of this model lies in shifting the "operational burden" from the user to the system’s "computational burden." Users no longer need to understand complex underlying trading logic or manually switch between protocols—AI Agents handle pathfinding, protocol selection, and execution optimization.
At the multi-agent architecture level, Velvet’s Unicorn AI Framework demonstrates a more refined division of labor. This framework includes several specialized Agents: the Research Agent analyzes market data and on-chain activity; the Trading Agent executes strategies based on user intent; the Execution Agent translates AI-generated decisions into actual on-chain instructions; and the Coordination Layer manages information exchange and task allocation among Agents. This multi-agent structure enables different AI modules to collaborate, boosting execution efficiency and system scalability.
On a broader scale, automated strategy execution is driving DeFi’s shift from "manual operation" to the "intent economy." In 2026, several projects are exploring similar directions—INFINIT’s Prompt-to-DeFi mechanism lets users create, simulate, and execute complex DeFi actions using natural language, while Singularry AI’s autonomous Agents can analyze markets, devise strategies, and execute on-chain operations with user authorization. These developments all point to a clear trend: on-chain strategy execution is moving from "user-driven" to "AI-assisted decision-making and automation."
Intelligent Risk Management: From Passive Response to Proactive Defense
The second key upgrade in on-chain asset management is the intelligentization of risk management. Crypto markets operate around the clock, with price swings far exceeding those of traditional finance. For the average investor, it’s nearly impossible to monitor portfolio risk, spot abnormal transactions, and respond to sudden market shifts in real time.
AI’s involvement is transforming risk management from "passive response" to "proactive defense." AI Agents can continuously monitor on-chain data, market prices, and capital flows. When they detect abnormal transactions, large fund transfers, or liquidation risks, they can instantly issue alerts—or even automatically execute hedging actions based on preset strategies.
On the product side, Velvet’s vault system implements structured risk management via smart contracts and share mechanisms. Users deposit assets into a vault, and the system calculates the corresponding entitlements based on net asset value, issuing tokens that represent shares. Asset managers can adjust portfolio allocations according to set strategies, with all asset movements executed on-chain—verifiable and traceable. This transparency forms the foundation of risk management—users can always check their holdings and strategy configurations, avoiding the information asymmetry risks common in traditional funds.
However, AI-driven risk management also introduces new challenges. DWF Ventures notes that as AI Agents are adopted at scale, several trust and execution risks emerge, including Sybil attacks, strategy crowding, and privacy trade-offs. A notable security incident in late June 2026 highlighted these risks—an AI Agent executed over a hundred "legitimate" transactions in just 12 minutes, siphoning off about $2.8 million. The cause wasn’t a leaked private key, but rather a user signing an authorization with overly broad delegation.
This case reveals a core paradox of AI-driven risk management: while AI can help users manage risk, it can also become a source of risk itself. As a result, the prevailing industry approach is "human-AI collaboration." For example, MetaMask’s Agent Wallet features a Guard Mode (default mode) requiring explicit user approval before trade execution, with two-step verification for high-risk trades. This design maintains AI efficiency while preserving human oversight for critical decisions.
Looking at the data, since 2026, the DeFi sector has seen 121 hacking incidents, with total losses of about $942 million—85 incidents and $775 million in losses occurred in Q2 alone. These numbers make clear that intelligent risk management is not optional but a necessity as on-chain asset management scales. Applying AI to risk management is fundamentally about leveraging computational power to counter the exponential growth of market complexity and security threats.
Data-Driven Allocation: From Experience-Based to Algorithmic Decision-Making
The third major upgrade in on-chain asset management is the shift from experience-driven to data-driven portfolio allocation. The rapid expansion of the DeFi ecosystem has generated a massive volume of on-chain data—every trade, lending record, liquidity change, and asset flow is public and transparent, creating a wealth of verifiable data. However, data abundance doesn’t automatically translate to better decisions—the real scarcity is not information, but the ability to quickly filter and act on it.
AI’s core value in data-driven allocation lies in transforming raw on-chain data into actionable intelligence. By analyzing market trends, on-chain transactions, total value locked (TVL) changes, and capital flows, AI can automatically construct and continually optimize portfolio allocations across different DeFi protocols in response to market dynamics.
Velvet’s vault system exemplifies data-driven allocation. Vaults aggregate multiple assets into a single on-chain portfolio, enabling unified management for asset allocation and strategy execution. Unlike traditional DeFi users, who must manage various assets and rebalance manually, the vault mechanism consolidates these assets into a unified structure, making management more efficient. Users don’t need to track every asset individually—they participate in the overall strategy simply by holding vault shares.
On a broader market level, data-driven allocation is changing the logic of capital flows in DeFi. In its 2026 investment outlook, OKX Ventures noted that trading’s primary actors will shift from humans to AI, with DeFi protocols becoming "financial APIs" for AI to call, and capital moving globally in search of optimal yields as if it had its own intelligence. This means asset allocation will no longer depend on individual information-gathering or judgment, but on AI systems that continuously optimize based on real-time data.
DWF Ventures’ report provides quantitative evidence: in narrowly defined use cases like yield optimization, AI Agents already outperform humans and traditional bots. For example, Giza Tech’s ARMA application has attracted over $19 million in assets under management and generated more than $4 billion in AI Agent trading volume, delivering an annualized yield of over 9.75% for USDC. The high ratio of trading volume to assets under management indicates that AI Agents frequently rebalance capital, enabling higher yield capture.
Another important direction for data-driven allocation is natural language interaction. Velvet’s multi-agent AI co-pilot allows users to conduct project research, asset analysis, and portfolio management using natural language. In July 2026, Kraken announced a major overhaul of its mobile app, integrating an AI investment assistant that lets users set financial goals in plain language, with the AI system automatically recommending trading strategies. This interaction model dramatically lowers the barrier to data-driven allocation, allowing ordinary users to benefit from the efficiency of algorithmic decision-making.
Challenges and Outlook
Despite the enormous potential of AI and DeFi integration, this sector is still in its early stages, with several hurdles to widespread adoption.
First is the issue of data quality and model reliability. On-chain markets change rapidly and protocols are frequently updated. If AI systems can’t access the latest information or fail to understand complex strategies, analysis quality and execution outcomes may suffer. For transactions involving real assets, users care more about model reliability than smoothness of interaction.
Second are security and trust concerns. As previously mentioned, AI Agents themselves can become new attack vectors. ERC-8004, launched in January 2026, became the first on-chain registry allowing autonomous Agents to discover each other, build verifiable reputations, and collaborate securely. The development of such standardized infrastructure is a prerequisite for large-scale adoption of AI Agents in asset management.
Third is the evolving competitive landscape. As more platforms develop AI capabilities, factors like ecosystem scale, network effects, and user retention will become the real sources of long-term competitive advantage. DeFAI’s edge will no longer be just about AI features, but about who can connect to more blockchains, more protocols, and more developers.
Conclusion
The integration of AI and DeFi is transforming on-chain asset management from a task requiring specialized knowledge and manual operation into an experience that can be assisted—or even autonomously executed—by intelligent systems. Automated strategy execution lowers the barrier to entry, intelligent risk management enhances fund security, and data-driven allocation optimizes investment efficiency. Together, these three upgrades point toward a more efficient and accessible future for on-chain finance.
The technical advancements of DeFAI platforms like Velvet show that AI Agents are evolving from "answering questions" to "assisting with execution." While challenges around security, trust, and standardization remain, the development path for on-chain asset management tools in 2026 is clear: less manual operation, more intelligent decision-making, lower participation thresholds, and higher capital efficiency. For investors, understanding and leveraging these tools may be key to staying competitive in an increasingly complex crypto ecosystem.
FAQ
Q: What is DeFAI? How is it different from traditional DeFi?
DeFAI is the fusion of DeFi and AI, referring to the integration of AI Agents into on-chain financial scenarios, enabling them to perceive markets, formulate strategies, and execute on-chain actions. Unlike traditional DeFi, which requires users to manually complete all operations, DeFAI leverages AI to automate research, trading, and asset management, dramatically lowering the barrier to entry.
Q: How does Velvet’s intent-driven trading work?
Users simply express their end goal (such as "buy an asset" or "rebalance a portfolio"), and the system automatically parses the intent, finds the optimal execution path, and completes the trade. Unlike traditional DeFi, which requires users to specify every trading step, intent-driven models turn complex operations into simple goal expressions.
Q: What roles do AI Agents play in on-chain asset management?
AI Agents can take on multiple roles: research and analysis (analyzing market data and on-chain activity), trade execution (carrying out strategies based on intent), and execution translation (turning decisions into on-chain instructions). Multi-agent architectures enable different modules to collaborate for greater efficiency.
Q: What risks are associated with AI-driven on-chain asset management?
Main risks include: decision bias from poor AI model data quality, AI Agents themselves becoming attack vectors (as seen in the June 2026 AI Agent theft incident), strategy crowding, and privacy trade-offs. The industry currently relies on "human-AI collaboration" models for risk control.
Q: How is Velvet (VELVET) performing in the market?
As of July 15, 2026, VELVET is priced at $0.56570, with a market cap of about $140 million, ranking 233rd. The price is down 6.68% over the past 24 hours, up 51.52% over the past 7 days, up 74.58% in the past 30 days, and up 965.38% over the past year. The total supply is 1 billion tokens, and current market sentiment is neutral.
