I. AI Agents Are the Unavoidable Global Trend in Technology.
AI is Evolving at an Unprecedented Pace, and One of the Most Disruptive Trends Today is AI Agents – Systems Capable of Autonomous Actions to Achieve Complex Goals. These advancements are not only transforming the way humans interact with AI but are also opening up countless opportunities for tech startups.
Previously, AI primarily served as a supportive tool to help humans process information more quickly. However, AI Agents are now approaching the ability to think and act like a real employee.
For example, an AI Agent in the finance sector can autonomously monitor the market, analyze price fluctuations, execute trades, and adjust investment portfolios without constant human intervention. Meanwhile, an AI Agent for e-commerce can manage inventory, place orders with suppliers when stock runs low, and optimize pricing strategies based on consumer trends.
This Shift Makes AI Agents More Than Just Support Tools, Turning Them Into Digital Workers Capable of Replacing or Assisting Humans Across Various Industries.
According to a report by McKinsey, companies typically allocate 70% of their budget to personnel, while software budgets account for less than 10%. However, AI Agents, which can perform tasks like a real employee, have the potential to tap into recruitment budgets rather than only competing in the software market.
This Unlocks Massive Business Opportunities:
- Businesses Can Replace or Supplement Their Workforce with AI Agents to Reduce Costs.
- Startups Can Develop Industry-Specific AI Agents for Sectors Ranging from Finance and Healthcare to Education and E-commerce.
Startups like Sierra (AI Customer Support), Norm AI (Compliance Automation), and Cognition AI (AI Software Engineer) Have Raised Hundreds of Millions of Dollars from Major Investment Funds, Demonstrating Strong Interest in This Sector.

II. AI Agents in Web2: Automating Everything from Business to Daily Life
1. AI Agents in Business: Revolutionizing Operations and Efficiency
AI Agents Are Playing an Increasingly Important Role in Business Operations, Automating Processes, Reducing Operational Costs, and Enhancing Work Efficiency. With Their Ability to Handle Massive Data, Learn from User Behavior, and Perform Tasks Autonomously, AI Agents Are Helping Businesses Optimize Key Areas Such as Sales, Marketing, Finance, and Risk Management.

A. Sales & Marketing Automation Industry: Enhancing Efficiency and Growth with AI

AI Agents Are Revolutionizing Sales and Marketing by Optimizing Advertising Strategies, Personalizing Customer Experiences, and Automating Customer Interaction Processes.

- Customer Data Analysis: AI can collect and analyze consumer behavior, suggesting targeted marketing campaigns for specific customer segments.
- Advertising Optimization: AI automatically adjusts ad content based on user interactions, campaign performance, and demographic data, helping businesses increase ROI.
- Automated Customer Interaction: AI can send automated messages, emails, or push notifications, maintaining efficient communication with customers.

Real-Life Examples:

- Apten_AI: An AI platform that automates SMS marketing processes, helping businesses boost customer conversion rates without manual staff involvement.
- Casixty: AI that scans Reddit data to detect trending topics and automatically creates interactive content, increasing brand visibility and reaching the target audience.
B. Finance & Accounting: Revolutionizing Financial Management with AI Agents
In Finance, AI Agents Help Mitigate Risks, Optimize Cash Flow Management, and Automate Accounting Tasks.
- Financial Fraud Detection: AI can scan millions of transactions daily to detect suspicious behaviors such as unusual transactions or money laundering.
- Automated Financial Reporting: AI can classify accounting data, compile tax reports, and forecast cash flow, enabling businesses to make more accurate financial decisions.
- Business Expense Management: AI can track and analyze spending, detect over-budget expenses, and recommend reasonable cost-saving measures.
Real-Life Example:
- Klarna AI Assistant: An AI assistant integrated into Klarna's financial system, helping analyze and automatically process over 2.3 million transactions monthly, saving labor costs for the business.
C. Risk Management & Compliance: Leveraging AI Agents for Enhanced Security and Regulatory Adherence
AI Agents Are Helping Businesses Comply with Complex Regulations, Reduce Compliance Risks, and Ensure Adherence to Financial Standards.
- Regulation Monitoring and Compliance: AI can analyze thousands of legal documents, helping businesses ensure industry compliance with minimal human intervention.
- Legal Risk Detection: AI can identify potential issues in contracts, transactions, or business processes, enabling businesses to proactively manage risks before facing penalties.
- Automated Internal Auditing: AI can compare financial data, detect discrepancies, and report anomalies, helping businesses enhance financial transparency.
Real-Life Example:
- Norm AI: An AI platform that helps businesses automatically comply with legal regulations by analyzing complex laws such as the Clean Air Act and Affordable Care Act, offering suggestions for policy adjustments.
2. AI Agents in Daily Life: Enhancing Convenience and Personalization
AI Agents Not Only Support Businesses but Are Also Becoming an Integral Part of Personal Life, Helping Individuals Optimize Tasks, Manage Time, Access Information More Accurately, and Enhance Quality of Life. Here Are Some Notable Applications of AI Agents in Daily Life:
A. Multi-Functional Virtual Assistants: Supporting Personal Tasks and Schedule Management
AI Agents Can Function as Intelligent Personal Assistants, Helping Users Automate Daily Tasks Without Manual Intervention. Here Are Some Examples of Tasks AI Agents Can Handle:
- Booking Flights, Renting Cars, and Online Shopping: AI can scan data from multiple sources, find the best fares, and make bookings that align with the user's schedule.
- Personal Schedule Management: AI can automatically sync data from emails, work calendars, and messages to create and adjust an optimized schedule, sending reminders for important appointments.
- Automating Administrative Tasks: AI can fill out forms, generate reports, organize documents, and perform other administrative duties as required.
Real-Life Example:
- Roboagent69 – A Personal AI Agent Capable of Managing Daily Schedules, Tracking Tasks, and Reminding Users of Important Deadlines. Users Simply Input Requests via Voice or Text, and the AI Handles Everything Without Repeated Interaction.
B. Sports Commentary & News Updates: Data Analysis and Automated Content Creation
AI Agents Not Only Help Individuals Organize Tasks but Also Play a Key Role in Updating and Analyzing Sports, Entertainment, Economic, and Financial News. Here Are Some Examples:
- Sports Data Analysis: AI can track team and player performance, match scores, and key statistics, providing predictions or in-depth analyses.
- Automated Content Creation: AI can write commentary, summarize match results, or even automatically generate highlight videos based on collected data.
- Personalized Content: AI can learn user preferences to recommend relevant content (e.g., news about a favorite sports team or tournaments they follow).
Real-Life Example:
- HeyTracyAI – An AI Agent specializing in analyzing NBA tournaments, offering sports commentary, match statistics, player comparisons, and predictions. AI can automatically write post-match commentary based on the analysis data.
C. Fake News Detection & Fact-Checking: Combating Misinformation on Social Media
AI Agents Can Help Users Verify the Accuracy of Information on Social Media, Preventing the Spread of Fake News and Misinformation. Here Are Some Examples:
- Scanning and Analyzing Social Media Data: AI can automatically collect and analyze content on platforms like Facebook, Twitter, Reddit, etc., to detect suspicious news.
- Cross-Referencing Information with Reliable Sources: AI can compare information with trusted sources such as mainstream news outlets or fact-checking organizations to validate its authenticity.
- Accuracy Alerts and Evaluation: AI can label content as reliable, needs verification, or potentially misleading, warning users about the accuracy of the news.
Real-Life Example:
- PerspectiveAI – An AI Agent specializing in verifying the authenticity of news on social media, helping users assess whether news is Fake News, thereby minimizing the risk of scams or being influenced by misleading information.
III. AI Agents in Web3: Shaping the Future of Decentralization
Web3 Not Only Leverages AI to Optimize Processes but Also Harnesses the Power of Blockchain to Create Autonomous AI Agents Capable of Managing Assets and Conducting Transactions Intelligently.
1. AI Agents in DeFi: Automating Transactions and Asset Management
AI Agents are Driving a Revolution in Decentralized Finance (DeFi) by Automating Trading Processes, Analyzing On-Chain Data, and Optimizing Profits. Instead of Users Continuously Monitoring the Market and Manually Adjusting Portfolios, AI Agents Can Make Real-Time Investment Decisions, Enhancing Trading Performance and Minimizing Risks.
A. Automating DeFi Transactions
One of the Key Applications of AI Agents in DeFi is Automating Transactions. AI Can:
- Analyze On-Chain Data: Track transactions, liquidity trends, price fluctuations, trading volume, and other on-chain metrics.
- Identify Trading Opportunities: Based on historical data and predictive models, AI can identify potential investment opportunities and suggest appropriate strategies.
- Execute Automated Transactions: When market conditions are right, AI will automatically place buy/sell token orders according to pre-set strategies, optimizing profits and minimizing slippage risks.
- Optimize Trading Strategies: AI can adjust strategies based on the latest market data, ensuring a higher win rate compared to manual trading.
Examples of AI Agent Projects in DeFi:
- Sendaifun – An AI Agent toolset on Solana, supporting asset management, capital allocation, and automated trading across DeFi protocols.
- Cod3xOrg – A no-code platform that allows users to create automated trading AI without the need for programming skills.
B. Optimizing Staking & Lending in DeFi
Beyond Trading, AI Agents Play a Crucial Role in Staking and Lending, Helping Users Maximize Profits from Idle Assets and Manage Liquidation Risks When Borrowing on DeFi Platforms.
- Automated Staking: AI can analyze and evaluate staking protocols to identify the highest Annual Percentage Yield (APY) and lowest risk ratios. When interest rates change or better opportunities arise, AI will automatically move assets to the optimal staking protocol, maximizing returns.
- Lending/Borrowing Optimization: AI can monitor user loans on platforms like Aave, Compound, and Venus. When collateral levels drop to risky thresholds, AI will automatically adjust (adding collateral or repaying debt) to prevent liquidation. Additionally, AI can analyze lending protocols to find the lowest borrowing interest rates, helping users save on borrowing costs.
2. AI Agents in DAO Governance: Optimizing Decisions and Strategies
Decentralized Autonomous Organizations (DAOs) represent one of the most crucial applications of Web3, where decisions are made through on-chain voting rather than through a centralized entity. However, this system still faces several challenges, such as large volumes of information, limited data analysis capabilities, and low voter participation rates. AI Agents are gradually becoming powerful tools that support DAOs in operating more efficiently by analyzing governance data, optimizing strategies, and automating decision-making processes.
AI can gather and analyze on-chain data to provide insights into DAO governance activities. Tasks include summarizing and analyzing governance proposals, tracking voting history, and detecting anomalies in voting behavior. In addition, AI Agents not only analyze data but can also propose optimal governance strategies. Some ways AI can assist include:
- Predicting voting outcomes: AI can forecast how votes might turn out based on historical patterns and voting behavior.
- Data-driven policy advice: AI can provide recommendations on governance policies based on data insights.
- Supporting governance proposals: AI can help identify and evaluate governance proposals by analyzing trends and potential impacts on the DAO.
Several projects are integrating AI into DAO governance to improve decision-making efficiency:
- ai16zdao: An experimental project that supports AI-assisted voting and governance strategy proposals, helping members easily access critical information and optimize the decision-making process.
- DeepDAO: Uses AI to analyze on-chain DAO data, ranking DAOs based on activity levels and governance performance.
- Kleros: A decentralized dispute resolution system that uses AI to assist in resolving on-chain disputes related to DAO governance.
By integrating AI into decentralized governance processes, DAOs can make more informed decisions, improve voter engagement, and automate key processes, ultimately leading to more efficient and effective governance.
3. AI Agent trong bảo mật web3: Tự động hóa kiểm soát & phát hiện lỗ hổng Smart Contract
Security has always been one of the biggest challenges in the Web3 ecosystem, especially in decentralized finance (DeFi) protocols, NFTs, DAOs, and blockchain platforms. While smart contracts offer transparency and operate autonomously, they can still have significant security vulnerabilities, leading to asset losses from hacks, logical errors, or transaction manipulation.
AI Agents are playing an increasingly critical role in automating the smart contract security audit process, helping to identify potential weaknesses before they are exploited by hackers. This not only helps protect user assets but also builds trust in the Web3 ecosystem.
How AI Agents Audit Smart Contract Security:
- Analyzing Smart Contract Source Code: AI scans the entire source code of smart contracts to identify common security vulnerabilities such as reentrancy attacks, integer overflow, front-running, and MEV (Miner Extractable Value) exploitation. It also checks the consistency and logic of the contract to ensure it functions as intended.
- Simulating & Detecting Errors in a Sandbox Environment: AI can create a simulated environment to mimic real-world attack scenarios. These simulations help to identify vulnerabilities that hackers could exploit, which might be difficult for humans to detect.
- Automated Risk Assessment and Security Reports: After scanning the source code and simulating attacks, the AI Agent ranks the severity of each vulnerability (High, Medium, Low). The system also suggests mitigation measures, helping developers fix issues before deploying the smart contract.
- Real-Time Alerts: AI can monitor on-chain transactions and issue immediate alerts if it detects suspicious activity such as flash loan attacks, rug pulls, or price manipulation.
Real-World Examples of AI Agents in Smart Contract Security:
- CertiK Agent: An AI-powered platform that specializes in smart contract auditing. CertiK combines machine learning with manual auditing to offer a comprehensive security assessment system.
- SolEng Agent: This AI analyzes GitHub repositories of projects, helping developers identify potential malicious code or vulnerabilities before smart contracts are deployed.
- BlockSec AI: A security system that uses AI to monitor on-chain transactions in real time, detecting abnormal transactions and sending early warnings to the community.
4. AI Agent in Web3 Security: Automating Control & Smart Contract Vulnerability Detection
Web3 Security: AI Agents Automating Smart Contract Vulnerability Detection & Auditing"
Security has always been one of the biggest challenges for the Web3 ecosystem, especially in DeFi protocols, NFTs, DAOs, and decentralized blockchain platforms. While smart contracts offer transparency and operate automatically, they can still have severe security vulnerabilities, leading to asset loss due to hacks, logical errors, or transaction manipulation.
AI Agents are playing an increasingly important role in automating the smart contract security auditing process, helping to detect potential weaknesses before hackers can exploit them. This not only protects user assets but also enhances trust in the Web3 ecosystem.
How AI Agents Audit Smart Contract Security:
- Analyzing Smart Contract Code: AI scans the entire smart contract source code to find common security vulnerabilities like reentrancy attacks, integer overflow, front-running, and MEV exploitation. It also checks contract consistency and logic, ensuring the smart contract executes exactly as intended.
- Simulating & Detecting Bugs in a Sandbox Environment: AI can create a simulated environment to mimic real-world attack scenarios. These simulations help uncover scenarios that hackers could exploit, which might be difficult for humans to detect.
- Automated Risk Assessment and Security Reporting: After scanning the source code and simulating attacks, the AI Agent ranks the risk level of each vulnerability (High, Medium, Low). The system also suggests corrective actions, helping developers fix issues before deploying the smart contract.
- Real-time Alerts: AI can monitor on-chain transactions and alert immediately when suspicious activities like flash loan attacks, rug pulls, or price manipulation are detected.
Examples of AI Agents Operating in the Market:
- CertiK Agent: An AI specializing in smart contract auditing, CertiK automatically scans and detects security vulnerabilities. It uses machine learning combined with manual audits to provide a comprehensive security rating system.
- SolEng Agent: This AI analyzes GitHub repositories of projects, helping developers identify malicious code or potential vulnerabilities before deploying smart contracts.
- BlockSec AI: A security system that uses AI to monitor on-chain transactions in real time, detecting abnormal activities and sending early warnings to the community.
IV. Can AI Agents in Web3 Compete with Web2?
AI Agents in Web3 are still in the early stages of development and have not yet gained the widespread popularity seen in AI applications in Web2. However, Web3 AI Agents offer several groundbreaking advantages that Web2 AI may find difficult to achieve:
1. Decentralization & Security:
AI in Web3 operates on decentralized platforms, free from control by a single company or organization. This reduces the risk of data manipulation, ensuring that information cannot be altered or exploited for commercial purposes without users' knowledge. Instead of relying on AI models from large corporations like OpenAI, Google, or Meta, users can verify the source code and data of AI within Web3.
2. Incentive Mechanism through Tokenomics:
In Web3, users are not just customers but can also become contributors to the development of AI. The tokenomics system incentivizes users to provide data, train AI models, and improve accuracy. Data contributors can earn rewards in the form of tokens, helping to build a self-sustaining AI ecosystem that continuously improves over time.
3. Integration with DeFi & Blockchain:
Web3 AI can analyze on-chain data, automate transactions, and optimize profits, capabilities that Web2 AI cannot achieve due to its lack of access to blockchain data. AI Agents in Web3 can perform complex financial tasks, such as managing DeFi portfolios based on market conditions, predicting token price trends using on-chain transaction data, and supporting DAO governance by analyzing voting data and proposing appropriate strategies. Web3 AI has the potential to create decentralized trading models, helping users automatically optimize their financial strategies.

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