Here's How AI Agents Work!
AI agents have taken off in recent months. According to CoinGecko’s 2024 Annual Crypto Industry Report, the market capitalization of Web3 agents skyrocketed 222% in Q4 2024—surging from $4.8 billion to $15.5 billion.
In fact, Coinbase Ventures predicts the agentic web will completely transform the cryptocurrency space, with autonomous AI agents launching dApps, transacting digital assets, and seamlessly interacting with other agents and humans without supervision. From streaming AI girl band music and providing autonomous crypto news updates to managing a $338 million hedge fund independently, these agents are quickly becoming one of the hottest trends in both Web2 and Web3.
But how do AI agents actually work? This article breaks down their inner mechanisms and core features, explaining how they operate autonomously. We’ll also dive into the security, compliance, and data privacy challenges that come with this evolving technology. Let’s dive in!
Core Components: LLMs, Vector Databases, and Tool Integration
AI agents rely on large language models (LLMs), vector databases, and tool-calling to function autonomously. These components work together to enable natural language understanding, contextual decision-making, and complex task execution, especially in the Web3 space.
Large Language Models (LLMs)
LLMs such as OpenAI's GPT, Google's Gemini, and Anthropic's Claude are integral to AI agents. While traditional LLMs rely solely on pre-existing training data, limiting their knowledge and reasoning, AI agents use tool-calling to access up-to-date information, streamline workflows, and autonomously break down complex tasks into manageable subtasks. This ability enhances their capacity to handle intricate challenges without human assistance.
Acting as the "brains" of AI agents, LLMs help interpret and generate human-like language, facilitating more natural and intuitive interactions. They enable agents to understand instructions accurately, produce relevant and precise outputs, and execute detailed planning and reasoning processes efficiently.
Vector Databases
With the global vector database market projected to grow at a 21.9% CAGR to $15.1 billion by 2034, this technology is crucial for generative AI models and agents alike.
Vector databases store complex data, such as text and images, as numerical vectors, allowing AI agents to grasp context and identify patterns by comparing similarities. Unlike traditional databases that organize data in structured tables and rely on exact matches, vector databases excel in tasks like recommendation systems and contextual searches. This adaptability makes them particularly valuable for AI agents, offering enhanced decision-making, personalization, and the ability to manage workflows autonomously.
By accessing and processing vast amounts of unstructured data, vector databases empower AI agents to perform advanced tasks, such as generating personalized content, providing nuanced recommendations, and managing workflows without supervision.
Integrations and Tool-Calling
Despite their strengths, LLM-powered AI agents can still be prone to errors and hallucinations when relying solely on training data. Integrating tool-calling capabilities helps address this issue by offloading error-prone tasks to specialized tools.
For example, rather than using an LLM for calculations—which could lead to inaccuracies—a calculator tool can be employed to ensure precise results. In the Web3 space, tool-calling allows AI agents to interface with dApps, smart contracts, oracles, bridges, and blockchain networks. These integrations enable real-time access to on-chain data, facilitate contract execution, manage crypto transfers, connect wallets to DeFi protocols, and more.
With tool-calling, ChainGPT’s Nova AI agent monitors trusted news sources, blockchains, and social media platforms to deliver critical Web3 updates to users with real-time data collection and deep analytical processing. It filters out low-value and irrelevant content, prioritizes the most important updates, and performs rigorous fact-checking autonomously—ensuring accuracy and relevance without human intervention.
Critical Features and Processes: Memory, Reasoning, and Long-Term Context
AI agents are distinguished by their ability to remember, reason, and maintain long-term context. These capabilities enable them to learn from past experiences, solve complex problems, and adapt to new situations without human input. Let's explore how these features enhance the effectiveness of AI agents.
Memory
Memory is a critical component that sets AI agents apart from basic chatbots. By retaining memory, agents can learn from their mistakes and apply past experiences to optimize future performance.
This feature allows AI agents to manage sophisticated, long-term tasks by recalling relevant past decisions, interactions, and data points. Like human memory, agents use selective memory to retain critical information while avoiding cognitive overload. This helps maintain efficiency, especially when managing multi-step or long-term processes.
A hedge fund AI agent can remember a known individual’s past decisions, social media activity, and interactions to replicate their behavior while managing investments. This is exactly what ElizaOS (formerly ai16z) aims to achieve in Web3, with its creator modeling it after a16z co-founder Marc Andreessen.
Reasoning
Reasoning is the logical framework that allows AI agents to make autonomous decisions. Advanced reasoning capabilities enable agents to break down problems into smaller components, plan iterative steps, assess progress, and adjust their approach as needed.
AI agents can reason within the context of tools, effectively interacting with their environment, selecting appropriate tools, and structuring tool calls accurately. Self-reflection mechanisms—internal feedback loops—allow agents to evaluate the accuracy, relevance, and quality of their actions, enhancing their problem-solving abilities and adaptability.
Long-Term Context
Long-term contextual awareness enables AI agents to understand their broader environment and adjust their workflows dynamically in response to changes. This feature relies on memory to retain relevant information over extended periods.
For instance, a yield farming AI agent not only monitors real-time APYs and yield data but also considers broader crypto and DeFi market trends, liquidity changes, and governance decisions. If a governance vote impacts staking rewards or liquidity incentives, the agent can adapt its strategy to optimize profits while mitigating risks.
This capability also benefits multi-agent systems by allowing them to maintain global context while executing tasks and subtasks in parallel, enhancing coordination and adaptability within decentralized ecosystems. With long-term context, projects like the Solana-based Griffain agent simplify Web3 interactions by automating digital asset trading, NFT minting, and wallet management, making the market more accessible and user-friendly.
Security, Compliance, and Data Privacy Considerations
While AI agents offer substantial workflow enhancements, they also present security, compliance, and data privacy challenges. Without robust access controls and monitoring, agents' broad access to information could expose sensitive data, including financial records, personal identification, customer details, or proprietary company insights.
Additionally, AI agents' autonomous learning and adaptability can sometimes lead to unintended behaviors. These actions may pose security risks, which may easily lead to a loss of funds in the Web3 space. An agent could, for example, mistakenly transfer crypto to the wrong address or inadvertently expose a user's private key or secret phrase. Regular monitoring is crucial to minimize these risks and maintain safety.
Regulatory compliance is another important aspect. AI agents in both Web2 and Web3 environments must adhere to data privacy regulations like the EU's GDPR and California's CCPA. These frameworks govern how personal data is managed, and ensuring compliance is vital to avoid legal repercussions.
The Year of Web3 AI Agents Is Here
AI agents reshape how tasks are completed, decisions are made, and digital ecosystems operate. By leveraging LLMs, vector databases, and advanced tool integration, they are evolving from simple bots into sophisticated, autonomous entities with reasoning, memory retention, and contextual awareness.
With key players like ChainGPT, Griffain, and ElizaOS leading the charge, 2025 is shaping up to be the year of the agentic web. Industry experts anticipate that over a million AI agents will populate the Web3 space in the coming months, significantly reshaping the digital economy.
While challenges around security, data privacy, and regulatory compliance remain, AI agents mark the next step in the evolution of decentralized economies. The real question now is: "how will Web3 transform once it fully embraces the agentic web?"
.jpg)
.png)
.jpg)
