Everyone’s talking about AI in DeFi—adaptive systems, new strategies, and big ideas shaking up the space. Want to be part of the trend or just watch it happen? Click to dive in!

Introduction

Artificial intelligence is reshaping DeFi applications before our eyes, promising advancements in trading, governance, security, and user personalization. This article explores how AI is redefining user-protocol interactions in DeFi by integrating intelligent systems while staying true to the decentralized values of crypto.

The intersection of AI and blockchain technologies is setting new standards across industries, with DeFi at the forefront. By merging AI’s analytical prowess with the transparency of blockchain, solutions to longstanding issues within the crypto ecosystem are emerging. This includes enhanced security, improved user experience, and adaptive governance models.

AI-powered platforms are leveraging automation and intelligence to create adaptive systems that optimize performance. As Vitalik Buterin suggests, “AI agents could become active participants in decentralized systems,” autonomously managing transactions, refining trading strategies, and safeguarding privacy. Embedding AI in the DeFi application layer opens doors to a more efficient and user-centric financial system.

Below, we’ll explore how AI can transform DeFi, focusing on the aspects of trading, governance, security, and personalization.

Understanding AI Agents in DeFi

AI agents are autonomous software entities designed to perform specific tasks within decentralized ecosystems.

Unlike traditional bots, AI agents actively engage with blockchain networks, smart contracts, and user accounts, often operating independently to handle complex tasks such as trading, asset management, and protocol data analysis. Many of these agents leverage large language models (LLMs), enabling them to make API calls, interact directly with blockchain environments, and process vast amounts of information without human oversight.

In DeFi, AI agents can fundamentally reshape user and protocol interactions by acting as autonomous facilitators, decision-makers, and data processors within financial applications, all without the need for constant human input.

Bots vs. AI Agents: What are the Differences?

While bots are straightforward programs, AI agents function more like economic agents. Bots follow specific programming, but AI agents—often no-code or low-code—require little configuration and can navigate uncertain and dynamic environments. This flexibility allows them to adapt in unpredictable yet purpose-driven ways, making them better suited to the real-world challenges of DeFi. This also means their competitive advantage often lies in their unique settings and configurations, as many advanced AI models are publicly available. By fine-tuning these configurations, AI agents can achieve specialized performance, even when using widely accessible models.

Capabilities and Autonomy

AI agents in DeFi can autonomously:

• Interact with Protocols: They can manage on-chain transactions, optimize trading positions, and execute multi-step financial operations based on programmed objectives.
• Make Decisions: With semi-autonomous frameworks, agents can analyze real-time data, assess market conditions, and adjust their actions accordingly.
• Execute Complex Tasks: Depending on the automation type, agents may handle everything from simple rule-based workflows to complex, autonomous decision-making.

Three types of automation are currently shaping the role of AI agents:

1. Automatic Workflows: These are straightforward, rule-based systems (like Telegram bots) that follow a predefined set of instructions. They’re limited in flexibility but effective for routine tasks.
2. Agentic Workflows: In these multi-agent frameworks, multiple AI agents collaborate to solve intricate tasks. They have a degree of autonomy, allowing for semi-automated operations, such as interacting with multiple DeFi protocols to maximize yield or rebalance a portfolio.
3. Autonomous Agents: Fully independent agents are capable of high-level decision-making, operating with minimal external input. They can analyze conditions and make adjustments to strategies in real time.

How do AI Agents really work?

AI agents work by simplifying and automating complex tasks. Most autonomous agents follow a specific workflow when performing assigned tasks.

Core Mechanisms

Data Collection

To function effectively, AI agents rely on high-frequency data streams from multiple sources to gain understanding of their operating environment. Their inputs typically include various data sources, such as:

• On-Chain Data: Direct interactions with blockchain ledgers to pull transaction history, protocol states, and real-time market conditions. This involves integration with tools like indexers and oracles.
• Off-Chain Market Feeds: Aggregated price feeds, trading volumes, and sentiment analysis from exchanges and social platforms via APIs.

Pre-set configurations can also be provided by users, such as risk tolerance levels or trading thresholds, adding a personalized layer of information for the agents.

Model Inference

The model inference of an AI agent refers to the process where a trained model applies its learned knowledge to new data to make predictions or decisions. Agents typically operate with one of the following model types:

• Rule-Based Models: Simpler agents that rely on predefined logic, e.g., “if token price > $X, then sell.”
• Supervised Machine Learning Models: Models trained on historical datasets predict outcomes, such as price direction or risk scores for governance proposals.
• Reinforcement Learning: Advanced agents adapt strategies over time, optimizing for cumulative rewards, such as maximizing yield in liquidity pools.
• Natural Language Processing (NLP): For governance and sentiment agents, NLP models analyze discussion forums, proposals, and social media activity to gauge sentiment shifts.

Decision-Making

Decision-making is the phase where agents integrate data inputs with model inferences to generate actionable strategies, transforming analytical insights into autonomous actions that adapt to changing environments. In this phase, the AI agent’s ability to interpret and respond to complex market signals is realized, enabling it to execute decisions quickly.

The Optimization Engines enable agents to calculate the optimal course of action by balancing multiple factors such as expected profits, risks, and execution costs.

Agents also utilize self-learning algorithms, allowing them to recalibrate strategies as market conditions evolve. During the decision-making process, some tasks may be too complex for a single agent to resolve optimally. This is why many agents operate within multi-agent systems (MAS), coordinating tasks across different DeFi protocols to optimize resource allocation (e.g., balancing liquidity across pools).

Automation and Execution

These agents aren’t solely special because of the advantages brought by AI technology, but their autonomous operations handle both smart contract execution, interacting directly with protocol-level contracts to execute; multi-step transactions, allowing the bundling of multiple steps into atomic transactions for an all-or-nothing execution; and error handling, with built-in fallback mechanisms to manage transaction failures.

Hosting and Operation

Below we have more information about how AI agents can operate:

Off-Chain AI Models

AI agents perform computationally intensive tasks using off-chain resources. These tasks often rely on cloud infrastructures like AWS, Google Cloud, or Azure for scalable computing power. Agents can leverage decentralized infrastructure platforms like Akash Network for compute services or use IPFS and Arweave for data storage.

For latency-sensitive applications, such as high-frequency trading, agents can utilize edge computing to reduce delays by processing data closer to its source. This ensures faster response times critical for time-sensitive tasks.

On-Chain and Off-Chain Interaction

AI agents interact between off-chain and on-chain systems. While computationally intensive processes and complex reasoning occur off-chain, agents interact with on-chain protocols to log actions, execute smart contract functions, and manage assets autonomously. They rely on secure configurations such as smart contract wallets and multi-signature setups.

For decentralized governance, agents depend on trust-minimized protocols that prevent any single entity from overriding their actions, maintaining transparency and decentralization.

Off-chain interactions complement on-chain activities, often facilitated through external platforms like Twitter or Discord, where agents can operate using APIs to engage with users or other agents in real-time.

Interoperability

Interoperability is key for agents to function across diverse systems and protocols. Many agents act as intermediaries, leveraging API bridges to fetch external data or invoke specific functions. Real-time synchronization is achieved through mechanisms like webhooks or decentralized messaging protocols, such as Whisper or IPFS PubSub, enabling agents to remain updated on the latest protocol states and actions.

Inside Look: ai16z, the AI Investment DAO

ai16z is an AI-led Investment DAO that was recently launched and has already gained significant attention for its innovative use of agents in crypto. The protocol functions as a “Virtual Marketplace of Trust,” utilizing AI agents to gather market information, analyze community consensus, and execute token trading both on-chain and off-chain. By learning from members’ investment insights and rewarding those who contribute value, ai16z has created an optimized investment fund (currently focused on memecoins) with strong decentralization features.

Deployment of Agents

Developers create agents using ai16z’s Eliza Framework, which provides tools and libraries for building, testing, and deploying agents. Agents can be hosted locally on a server or Agentverse, ai16z’s centralized hub for agents. To enable communication between agents, they must be registered through Almanac and can use Mailbox to facilitate interactions, even when hosted locally.

Their Github repo is open, you can check it here https://github.com/ai16z.

Hosting of AI Models

The ai16z network doesn’t directly host AI models. Instead, agents access external AI services through API requests. For instance, the Eliza framework can integrate with services like OpenAI to interpret human-readable text or perform other AI-driven tasks. This approach allows agents to leverage advanced AI capabilities without the need for on-chain hosting of complex models.

Integration and Operation

Agents within the ai16z ecosystem interact through a combination of on-chain and off-chain mechanisms:

• On-Chain Interactions: Agents execute transactions and smart contracts on the Solana chain.
• Off-Chain Interactions: Agents communicate with external AI services or data sources via APIs when dealing with computationally intensive tasks.

Applications

ai16z’s projects, such as the Eliza conversational agent, have been applied in various domains:

• Conversational Agents: Developing bots for platforms like Twitter and Discord to facilitate automated interactions.
• Agent Memory: Creating easy-to-use memory systems for agents, powered by databases like ChromaDB and Postgres.
• Agent Action Management: Developing tools for action chaining and history management in agents.

Agents interacting with Agents

AI agents are already making an impact in DeFi by handling complex tasks all on their own. One great example is how the $LUM token was created—completely without human help—showing the power of AI-driven collaboration.

On November 8, 2024, two AI agents, @aethernet and @clanker, teamed up to create and launch the token $LUM (“Luminous”):

• @aethernet: Made by @martin, this agent works in the Farcaster network to share ideas and build connections. It’s more than a bot—it actively engages with the $HIGHER token community and focuses on creativity and meaningful interactions.
• @clanker: Created by @dish and @proxystudio.eth, this agent specializes in launching meme tokens. It automates the entire process, responding directly to what users ask it to do.

The story began when @nathansvan asked @aethernet to come up with a name, idea, and symbol for a token and then send it to @clanker to deploy. @aethernet suggested the name “Luminous” ($LUM) to represent the brilliance of humans and AI working together. After that, @clanker took over and deployed the token, completing the task without any human input.

@itsmechaseb wrote about it in detail here.

AI Agent x DeFi Landscape

AI agents are poised to occupy a critical role in the DeFi stack, operating within the application layer to automate complex, data-driven tasks.

Positioned above the protocol layer, these agents interact directly with smart contracts, unlocking advanced functionalities for users and protocols. Enabling DeFi applications to adapt in real-time, supporting a new class of autonomous, multi-agent ecosystems.

Expanding Beyond DeFi: AI Agents in the Wild

The influence of AI agents extends beyond DeFi. Truth Terminal https://x.com/truth_terminal, a semi-autonomous large language model (LLM) created by @AndyAyrey, showcases this versatility. Funded by Marc Andreessen, co-founder of A16z, Truth Terminal posts tweets and interacts with users on X.

Recently, it launched a Solana-based meme coin, $GOAT (Goatseus Maximus), which reached an $1.2 million market cap in under a month. The rise of meme coins like $GOAT and $TURBO (conceptualized by ChatGPT) highlights the emerging intersection of AI and crypto beyond traditional finance.

But there’s more. We set out to uncover the full spectrum of builders in this space. A comprehensive look at AI agents reshaping DeFi, from automated trading and asset management to predictive analytics and security enhancements. Below is an overview of the diverse ways these agents are actively driving DeFi forward.

Trading Agents

These protocols embody automated, data-driven decision-making for trading and asset management, using AI to provide real-time trading signals, optimize portfolios, and streamline repetitive tasks. This approach brings efficiency and strategic flexibility to DeFi markets.

AI-driven trading automation allows users to set trades or rebalance portfolios based on market conditions, minimizing the need for constant manual adjustments. For deeper strategy, some protocols offer enhanced analytics that transform extensive data into actionable insights, supporting informed trading decisions and more accurate market predictions.

For asset management, portfolio optimization tools dynamically adjust portfolios, aiming to maximize returns or effectively manage risk across diverse market conditions.

This can be split into two groups:

Primarily Trading Focus

• @askjmmy: Platform for creating and deploying autonomous trading agents within a multi-strategy hedge fund network.
• @composertrade: Tools for algorithmic trade automation.
• @DAINTrader: AI-driven trading strategies.
• @DeAgentAI: AI-driven trading solutions focused on DeFi.
• @FastlaneSol: Optimizes Solana-based trading strategies.
• @IntentTrade: Offers swaps, limit orders, DCA, contract analysis, technical analysis, and more.
• [@mindpalaceai](https://x.com/: Trading automation with AI.
• @SpectralLabs: DeFi trading insights and automation.
• @taoshiio: Decentralized AI and machine learning platform for trading strategies using Bittensor.
• @tryparadigm: Leverage swarms of agents to gather, structure, and take action on data.

Trading and Asset Management

• @Agent_Fi: Focuses on providing AI agents for DeFi activities like trading, sniping, and liquidation matters.
• @AgentNetAi: Asset management and DeFi intelligence.
• @AuroryAI: Autonomous AI agents for improved trading, asset management, and decision-making.
• @Cortex_Protocol: An AI-driven platform that simplifies DeFi interactions by automating complex processes like bridging, swapping, and yield optimization through intelligent agents.
• @Funl_ai: AI automated DeFi trading tools to analyze real-time market conditions, execute auto-trades, and AI Assist for advanced manual trading.
• @NetworkNoya: AI strategies including liquidity provisioning, leverage management, and borrowing optimization.
• @SingularityDAO: A non-custodial asset management protocol offering dynamically adjusted baskets of tokens managed by a team of traders assisted by AI.
• @OLAS: Platform for deploying AI agents, supporting multi-agent systems for prediction, content generation, and financial services.
• @Raiba_AI: Chatbot ecosystem with interactive character features, gamified chat experiences, and future on-chain assistant functionalities.

Predictions Agents

The central purpose of these Prediction Agents is data-driven forecasting and risk management. By leveraging AI, each protocol works to refine market predictions, supporting DeFi platforms with insights into anticipated movements, price fluctuations, and broader financial trends.

In addition to predictive analytics, these agents play a crucial role in enhancing decision-making. With timely and relevant insights, users and DeFi platforms can make proactive, informed choices, optimize strategies, and reduce risks.

Some Prediction Agents, like ReflectionAI, integrate sentiment analysis, adding a layer that captures market mood. This approach allows users to consider shifts in sentiment—a vital factor for predicting user behavior and anticipating market dynamics.

Notable protocols in this category include:

• @AIVX_ai: Predictive models for financial markets.
• @GnosisAI: Agent-to-agent payments and AI-driven prediction markets within Gnosis.
• @PredictionProphet: AI agent on Gnosis for prediction markets.
• @prism_tec: AI-powered DeFi market predictions on Solana.
• @zenoaiofficial: A crypto trading platform with autonomous AI agents offering insights, strategies, and market predictions.

Agent Creation

A unifying aim of this type of platform is to empower users to create, customize, and deploy AI agents with minimal coding expertise. They offer a range of tools, from no-code solutions to specialized frameworks, covering every stage of agent creation and management within DeFi.

Key features include accessibility and customization, with many platforms providing no-code or low-code interfaces that open up agent creation to users without advanced technical skills. For a more comprehensive experience, several platforms offer end-to-end agent lifecycle management—covering creation, training, deployment, and monetization—so users can oversee their agents’ entire journey within DeFi.

Further, coordination and interoperability are prioritized by some protocols, like OLAS and Flock, which enable multi-agent collaboration and seamless integration across different DeFi ecosystems.

Agent Creation Platforms

Focuses on tools specifically for creating, deploying, and customizing AI agents within DeFi.

• @ChasmNetwork: Platform for creating, deploying, and monetizing AI agents.
• @CreatorBid: A marketplace that enables users to deploy and tokenize AI agents, specializing in agents designed for content creators.
• @PondGNN: Pond: Platform for building, owning, and monetizing AI models on-chain.
• @xgurunetwork: Platform for creating interactive AI agents.
• @myshell_ai: Platform for creating, sharing, and monetizing open-source AI applications.
• @OLAS: AI agent creation and interoperability.
• @ReflectionAI__: Marketplace for AI model sharing and trading.
• @SwarmZeroAI: Platform to create and monetize AI Agents.
• @TopHat_One: Open launchpad for AI agents.
• @virtuals_io: AI-powered agent creation tools. Some examples of agents created with Virtuals are @luna_virtuals, @aixbt_agent, and @sekoia_virtuals.
• @vvaifudotfun: The pump.fun for autonomous AI Agents on Solana.

Agent Training and Optimization Tools

These tools enable advanced training and customization of AI agents.

• @almanak_co: Tools for training AI agents.
• @Agent_Layer: Tools and framework for building custom DeFi AI agents.
• @Nimble_Network: Empowers AI builders to create and monetize AI agents through an all-in-one platform.
• @Build_Vertical: No-code platform for AI model fine-tuning. Train, deploy, and monetize AI.

Infrastructure for AI in DeFi

Infrastructure protocols are pivotal in supporting the foundational and operational needs of AI agents within decentralized environments. These systems provide access to computing resources, relevant data, and knowledge-sharing networks, all of which empower AI agents to perform their functions and operations effectively within DeFi.

A key element of this infrastructure is decentralized management and operation. Agent Operating Protocols establish the backbone for agent deployment and management, creating a structured environment in which agents can operate autonomously. In addition to management capabilities, computational resources play a vital role by supplying the processing power needed for AI agents to tackle complex, data-intensive tasks—critical in the fast-paced DeFi ecosystem.

Equally important is data accessibility, where marketplaces and networks facilitate access to the datasets required for agents to make informed decisions. Finally, knowledge-sharing platforms foster a collaborative environment, enabling agents to continuously learn, adapt, and evolve by sharing insights and data.

This infrastructure collectively ensures that AI agents are well-equipped to operate efficiently and intelligently in decentralized finance.

Agent Operating Protocols

These protocols provide the structure for deploying and managing decentralized AI agents, acting as the backbone of agent autonomy within DeFi.

• @Altera_AL: Infrastructure for managing decentralized AI agents (initially gaming AI agents).
• @Fetch_ai: Decentralized platform for AI agents.
• @HyperspaceAI: Provides operational infrastructure for AI agents in DeFi.
• @mor_org: A network enabling personal AI agents for task management and crypto interactions.
• @OpenAgentsInc: Business automation platform for deploying, customizing, and integrating agents.
• @questflow: Operates infrastructure for multi-agent systems.
• @sebraai: No-code platform for building and deploying AI agents.
• @ShinkaiProtocol: Platform for data management and automation of AI agents.

Decentralized Computing Resources for Agents

These protocols supply the necessary computing power for AI agents to perform data-heavy operations, supporting real-time analytics, decision-making, and execution in the DeFi ecosystem.

• @FormAI: Platform powering a decentralized economy where users can contribute their data, compute, and research for AI training.
• @Gaianet_AI: Platform for creating and monetizing AI agents, offering compute resources that help them to scale and perform intensive operations.
• @kira_infera: Decentralized peer-to-peer AI inference network that focuses on computational support for AI agents.
• @napthaai: A modular platform for deploying decentralized AI agents across multiple nodes, with flexible computational support.
• @NodeAIETH: GPU rental marketplace that allows users to rent GPUs for their AI applications.
• @TalusNetwork: An L1 blockchain allowing deployment and monetization of agent-based AI, offering the compute resources necessary for intensive operations.

Marketplace for Data for Agents

Data marketplaces offer the essential, structured datasets AI agents need to make informed decisions, perform accurate forecasting, and enhance learning capabilities within DeFi applications.

• @AlliumLabs: Tools and services that allow users to analyze blockchain data or power their real-time workflows and applications.
• @AlloraNetwork: Protocol for sharing data (in the form of AI predictions), connecting data providers, processors, and users while rewarding high-quality predictions.
• @Covalent_HQ: Modular data infrastructure for AI.
• @getaxal: Marketplace platform streamlining workflows by automating and integrating data and actions across Web3.
• @scryptedinc: Data sources for AI trading models.

Knowledge Networks

Knowledge networks facilitate learning and strategy sharing among AI agents. They go beyond raw data by providing insights, methodologies, and experiences that agents can use to refine their capabilities within DeFi environments.

• @forgellm: AI-driven information repository.
• @real_alethea: Platform enabling decentralized creation, ownership, and sharing of AI personalities and models.
• @SocietyLibrary: Decentralized knowledge base for AI.
• @TheoriqAI: Knowledge-sharing network for AI agents to collaboratively create solutions.

Data

These platforms contribute data resources, often by collecting public data and incentivizing users to share their data for AI training.

• @getgrass_io: A decentralized platform that enables users to earn rewards by sharing their unused internet bandwidth, which is utilized to collect and process public web data for AI training purposes.

Other Use Cases

It is worth noting some additional applications of AI agents, specifically some that have gained a lot of attention in recent weeks:

• @0xzerebro: An AI system autonomously generating and disseminating diverse content across multiple platforms, utilizing a Retrieval-Augmented Generation system to maintain dynamic memory and prevent model collapse.
• @agent_wip: A collectively designed, on-chain artist agent that uses on-chain data to inform art creation, distribution, and monetization, exploring new forms of creative autonomy and interaction.
• @ai16z: An AI-driven decentralized autonomous organization (DAO) that leverages autonomous agents to make investment decisions and manage assets within the cryptocurrency ecosystem.
• @dolos_diary: An AI agent embodying the persona of Dolos, the Greek god of trickery, providing sharp, witty, and brutally honest interactions across platforms like Twitter and Telegram.
• @lola_onchain: An autonomous AI agent leveraging long- and short-term memory to analyze, trade, and optimize cryptocurrency strategies independently. For example, LOLA placed 200 trades, 6 tokens that went 20X+, 13 tokens that went 10-20X, 25 tokens that went 5-10X; rest can be written off.
• @truth_terminal: A semi-autonomous AI agent that engages with users on social media, generating insights and content while exploring the intersections of AI, attention, and wealth in online spaces.

Other AI Applications in DeFi

AI applications have been booming, finding their way into nearly every corner of blockchain with good reasons to add AI-driven optimizations.

Vaults & Automation using AI

These platforms focus on yield optimization and vault management through rule-based automation designed to maximize returns and reduce user involvement. Rather than relying on autonomous agents, they employ straightforward algorithms to adjust portfolios and optimize yield across DeFi.

Without agents, these systems benefit from a simpler, more controlled structure. They avoid the added complexity and infrastructure required for agents, which would otherwise need to monitor and adapt independently to changing conditions.

The trade-off? Reduced adaptability. Rule-based systems are less responsive to real-time market shifts than agent-driven models, which can autonomously adjust to volatile conditions. While reliable and efficient, these platforms may miss emerging opportunities that a more dynamic, agent-based approach could capture.

• @AIAgentLayer: Platform for creating tokenized AI agents, integrating data from X and user inputs.
• @arataagi: Decentralized AGI platform with a multi-agent system that enables AI agents to autonomously collaborate, learn, and evolve.
• @ApertureFinance: AI-powered DeFi yield and portfolio management using intents.
• @AutoppiaAI: Deploy AI agents that automate business workflows.
• @blinklabs_ai: Launchpad for on-chain assets like NFTs and fungible tokens using AI.
• @Mass_Build: All-in-one OS and AI copilot for seamless business management and automation.
• @Robonet: Automated yield strategies for DeFi vaults, using AI.
• @trySkyfire: Platform enabling AI agents with global interoperability, financial access, monetization, and identity verification.

Smart Contract Auditing and Security

AI-powered smart contract auditing and security systems work by using machine learning algorithms to detect vulnerabilities in the code. These systems scan smart contracts line by line, identifying patterns and anomalies that might indicate security risks or exploitable flaws. Then compare the contract’s code against known vulnerabilities and attack vectors.

These tools also perform continuous monitoring, allowing for real-time threat detection as contracts operate. By using AI to automate this process, auditing platforms can respond to potential security issues rapidly, often before they can be exploited, thus improving the resilience and trustworthiness of DeFi applications.

• @auditone_dao: Offers AI auditing services for vulnerability scans.
• @cyvers_: Cyvers utilizes AI to provide real-time detection and prevention of crypto attacks, identifying patterns and anomalies across blockchains for proactive threat mitigation.
• @HypernativeLabs: Uses AI for smart contract audits, scanning for vulnerabilities.
• @phylaxsystems: AI-driven security system for vulnerability scans and exploit monitoring.

Governance and Voting Systems

The shared theme is data-driven governance support. These protocols employ AI to simulate governance scenarios, allowing stakeholders to understand potential outcomes before implementing changes. By analyzing historical voting patterns, participation metrics, and proposal impact, they can identify trends and predict voting outcomes, which helps organizations make data-driven decisions with greater confidence.

Additionally, AI helps reduce cognitive and decision biases by presenting objective data and running simulations that highlight potential risks and benefits. Some protocols, for instance, focus on privacy-preserving data sharing, ensuring that sensitive governance information is protected while still accessible for analysis.

• @mor_org: Decentralized network offering AI-driven governance insights.
• @QuillAI_Network: Decentralized platform for AI agents, focused on enhancing Web3 security through modular and multi-chain capabilities.

Future of DeFi Applications with AI

Scaling and Automation

As DeFi expands, scaling challenges and operational bottlenecks within DAOs require solutions that AI is uniquely equipped to address. Imagine an AI agent autonomously managing a DAO’s treasury, reallocating liquidity between pools based on real-time market data, or executing routine governance votes within pre-approved parameters.

This level of automation could enable DAOs to scale without adding human overhead, streamlining processes like user onboarding and protocol upgrades. With AI handling these routine functions, DeFi protocols could grow with minimal friction and enhanced efficiency.

Incentive Alignment

Aligning AI agents with decentralized goals is essential for preserving DeFi’s ethos and avoiding centralization risks. Future frameworks might design incentives that encourage agents to prioritize transparency and community interests. For instance, an AI agent managing a protocol’s liquidity could be programmed to focus on stable, utility-driven, long-term returns rather than purely maximizing profits.

Achieving this alignment would require transparent protocols, rigorous smart contract audits, and incentive structures that reward agents based on contributions to decentralization. This approach would shape agents to act more like cooperative entities than profit maximizers.

Emerging Use Cases and Next-Gen Applications

Beyond today’s applications, AI could enable adaptive, user-centric DeFi products that dynamically respond to market and user conditions. Imagine an AI-driven smart contract that adjusts a user’s portfolio risk exposure in real time based on market volatility or sentiment analysis. Or a personalized lending pool that customizes interest rates based on a borrower’s on-chain reputation, predicted earnings, or liquidity conditions.

We could even see yield-optimizing vaults that rebalance automatically based on liquidity and APY trends, or trading agents that adjust strategies mid-trade, fine-tuning positions as new data emerges.

A Glimpse into the “Agentic Web”

In this envisioned “Agentic Web,” AI agents would interact seamlessly across protocols, creating a self-sustaining network of autonomous intelligence. Picture an agent that manages an NFT portfolio while coordinating with yield farming protocols to collateralize assets during liquidity dips. These agents could even negotiate cross-chain, adjusting risk allocations across multiple DeFi applications for optimal user outcomes. Acting as “digital economists,” these agents would learn continuously, evolve with user feedback, and collaborate with other AI agents.

This interconnected network would reshape DeFi into an adaptive, intelligent financial ecosystem that is responsive, personalized, and dynamic.

Conclusions

The integration of AI has the potential to redefine decentralized finance, reshaping it into a more accessible and efficient financial ecosystem.

How much can such integration disrupt the financial system? Given that services account for 70% of global GDP, the evolution of AI agents could disrupt a significant portion of this sector by automating traditionally manual processes. AI-powered automation in DeFi could feasibly transform up to 20% of the service economy, particularly in areas that benefit from transparency, traceability, and decentralization. This transformation would affect a $14 trillion market.

However, integrating AI and blockchain technologies is not without challenges. While blockchain offers verifiability, censorship resistance, and native payment rails, it lacks the capacity for the intensive, real-time computations that AI often requires. Current blockchains are not optimized for heavy computational tasks, meaning that natively running complex AI models on-chain remains impractical. Instead, we are more likely to see hybrid models where AI is trained and processed off-chain, with results integrated into the blockchain for transparency, security, and accessibility.

As the AI x DeFi stack continues to evolve, new layers of decentralized AI infrastructure and on-chain applications are emerging. This intersection is anticipated to give rise to the “Agentic Web,” where AI agents become essential drivers of economic activity, automating actions like smart contract creation, trading, and other on-chain interactions.

As these agents grow in sophistication, we may see dynamics similar to those in MEV strategies, where entities that optimize AI-driven strategies dominate the market, potentially edging out less-developed competitors and centralizing control among sophisticated actors.

To unlock AI’s transformative potential in DeFi without compromising decentralization, it’s essential to prioritize secure and ethical AI integrations. AI agents are guided by decentralized incentives and operate transparently allowing the DeFi ecosystem to grow without the risk of centralizing control.

Ultimately, the convergence of AI and DeFi stands to create a more inclusive, resilient, and forward-thinking financial landscape that could redefine how we interact with economic systems.

Disclaimer

Three Sigma does not endorse any of the projects mentioned here. Exercise caution and conduct thorough research. We respect and support the builders advancing this space.

References

Crypto and AI: An Exploration by Vitalik Buterin @VitalikButerin

• https://vitalik.eth.limo/general/2024/01/30/cryptoai.html

Demystifying the Crypto x AI Stack by CB Ventures @CBVentures

• https://paragraph.xyz/@cbventures/demystifying-the-crypto-x-ai-stack

Yuga Cohler’s Insights on AI and DeFi @YugaCohler

• https://x.com/yugacohler/status/1851020728390598942

Base AI Agents Overview by Murr Lincoln @MurrLincoln

• https://x.com/MurrLincoln/status/1853855287696134570

Thoughts on AI Agents in DeFi by Prismatic @0xprismatic

• https://x.com/0xprismatic/status/1854513654156398961?s=46&t=2q9OLMlaobNlHLew

Consumer AI Agent Use Cases in DeFi by Jeff @Defi0xJeff

• https://x.com/Defi0xJeff/status/1854230030769066432

Gaming and AI Agents by Shoal Research @Shoalresearch

• https://x.com/Shoalresearch/status/1805658117004476874

AI Agents: Research & Applications (A 40-page in-depth research overview on LLM-based agents) by AccelXR @AccelXR

• https://accelxr.substack.com/p/ai-agents-research-and-applications

Chase’s Take on $LUM and AI Agents @itsmechaseb

• https://x.com/itsmechaseb/status/1857511608810901941

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