The writing team includes Lincoln Murr (Coinbase), Stefano Bury (Virtuals), Rishin Sharma (Solana), Pilar Rodriguez (The Graph), David Mehi (Google Cloud), and Cambrian members Ariel, Brian, Doug, Jason, Ricky, and Tumay.
Agentic Finance is reaching a critical tipping point, holding tremendous economic potential for those who enhance their financial behaviors with intelligent agents. AI agents are a type of autonomous tool capable of data analysis, decision-making, and trade execution, with operational methods covering different degrees of human involvement. Currently, these agent tools are being made accessible to the public, gradually disrupting a financial system long dominated by Wall Street and its high-frequency algorithms.
This article focuses on the retail applications of agentic finance in decentralized finance (DeFi), comprehensively sorting out the automated agent projects that have been launched and are dedicated to providing services for individual users. To this end, the project team conducted long-term research and interviews with dozens of teams in the industry, ultimately compiling a rigorously selected list of active projects, categorized by product type, with representative products annotated for each category.
Agentic finance is driving the maturity of the crypto industry, providing real-time information, professional-level advice, and optimizing user experience, making participation more efficient and reliable for ordinary users in DeFi. Here is a structured overview of the current ecosystem:
What is Agentic Finance (AgentFi)?
Agentic Finance refers to a new category of financial products that actively manage user funds or provide personalized financial advice using AI or machine learning. Some products leverage large language models (LLMs) for interaction and analysis, while others rely on rule engines or traditional machine learning algorithms. Despite the differing underlying technological paths, they collectively refer to themselves as 'agentic' products.
Currently, Agentic Finance is in the innovator stage, still at the starting point of the early adopter curve. Soon, various agents and AI assistants will dominate financial activities. Source: Ramp
However, it can be foreseen that in the near future, traders, asset managers, financial analysts, and other professionals will enhance efficiency using dedicated intelligent agent tools, while automated agent versions aimed at ordinary users will also be launched simultaneously. This trend has already begun to manifest: for example, on the Solana network, automated trading robots now account for over half of the trading volume¹.
Autonomy vs Intelligence: The capability coordinate system of AgentFi
Different agentic projects are distributed along the 'autonomy - intelligence' coordinate system based on their service scenarios and technical capabilities.
The horizontal axis represents the degree of intelligence: the left side consists of rule-based and statistical model tools, the middle includes traditional machine learning models, and the right side features advanced agents based on large language models (LLMs) or subsequent technologies;
The vertical axis represents the degree of autonomy: the bottom consists of 'advisory agents' that provide only suggestions and analysis, the top consists of 'fully automated agents' with complete decision-making and execution authority, and the middle represents a hybrid architecture with 'human-in-the-loop.'
When it comes to agentic finance, many people associate it with 'invisible robots' or advanced LLM systems capable of automated trading and independent portfolio management. However, in reality, such systems have not been deployed on a large scale due to the ongoing instability issues with LLMs. For instance, LLMs can still 'hallucinate' false information, and only recently have they developed basic counting capabilities (such as counting how many 'r' letters are in 'strawberry'). Currently, most agents only use LLMs for human-computer interaction interfaces or data analysis layers, while the fund management part still primarily relies on mature statistical models or machine learning algorithms that have been used for decades in traditional finance (TradFi).
From the development path of LLMs, their weaknesses in handling numbers and logical reasoning have historical reasons—they were initially designed for language prediction. But this situation is changing rapidly. For example, Anthropic has launched financial products adopted by institutions, while OpenAI has trained models competitive in the International Mathematical Olympiad.
Agentic Finance Project Overview for 2025
Below is a list of currently launched agentic projects with fund management capabilities that are open to users. Projects in development or internal testing phases are not included, nor are products that only use LLMs as interfaces but require user manual decision-making, thus many projects are not included in this round-up.
Trading and asset allocation agents
Trading agents are the type of agentic financial products most commonly associated with the public. These agents manage user funds by automatically adjusting positions or selecting assets to buy and sell. To achieve automated trading, agent systems typically need to have trading permissions, asset access, budget management, preset strategies, and high-quality data as components. Here is a list of current projects supporting one or more functions:
According to a recent poll initiated by Cambrian on platform X, most users have shown a high interest in high-risk trading agents.
Liquidity provision (LP) agents
Decentralized exchanges (DEX) rely on third-party liquidity providers (LP) to provide tradable assets, and the transaction fees paid by traders are received by LPs. The income of LPs depends on various factors, including impermanent loss, trading volume, DEX protocol incentives, etc. The following agent tools can help LP identify optimal liquidity allocation paths:
Lending agents
In the crypto market, users can earn interest by providing assets to borrowers. Lending agents typically need to assess factors such as yield, risk exposure, and opportunity cost when deciding whether to participate in lending agreements. Here are some of the launched lending agent projects:
Prediction and betting type agents
Prediction markets allow users to bet on the outcomes of future events, such as elections or sports events. These markets usually rely on real-time tracking of news or real-world information, which can change at any time. Prediction markets naturally align with agentic participation mechanisms, which Vitalik Buterin emphasized in his proposed concept of information finance (InfoFi).
Sentiment, fundamentals, news, technical analysis agents
Investors typically rely on market analysis to determine 'what to buy' and use sentiment analysis to decide 'when to buy or sell.' LLMs demonstrate transformative value in such analysis: not only do they significantly expand the scale and speed of analyzable data, but they also enhance contextual understanding by providing more comprehensive insights through identifying correlations between data sources.
Unlike the aforementioned executable trading agents, analytical agents only provide informational support and do not execute operations directly. Here are some representative projects among them:
It is worth noting that the Agentic Finance ecosystem is evolving rapidly, and existing projects are continually expanding their business boundaries. For instance, a product currently classified as a lending agent may expand into areas such as liquidity management in the future.
Future trends of Agentic Finance
On-chain assets are continuously growing, and the trading volume of on-chain stablecoins has reached a new high. Traditional fintech companies are also connecting to on-chain infrastructure. For example, Robinhood recently launched a tokenization service for U.S. stocks, enabling 24/7 on-chain trading open to global investors.
The crypto industry is gradually moving beyond the narrative of 'speculative trading' towards a broader application scene that includes investment functions.
However, for many users, successfully participating in DeFi still presents significant barriers. This is precisely where agentic products come into play: they are expected to significantly enhance usability and profitability, becoming a key driver for the popularization of DeFi.
Agentic Finance is a brand new market segment, and the tools mentioned above represent the first attempts in both TradFi and DeFi. We anticipate that some of these early projects may not achieve their visions, but the overall ecosystem will continue to mature. Ultimately, using agents will become the mainstream method of financial participation, and those users who take the first step into 'agentic finance' are more likely to gain long-term returns.
Moreover, as developers continue to deliver stable returns, users' attention to the details of agent strategies will decrease. In the future, agents may further integrate various capabilities (such as simultaneously managing trading and LP positions) to enhance complexity and efficiency.
Future focus areas
Future discussions may delve into the following related topics:
Agent-to-Agent (A2A) communication and payment mechanisms
Agent infrastructure and development frameworks
Data infrastructure and on-chain indexers
On-chain identity management
Agent issuance platforms and markets
Privacy and verifiability
Financial modeling and simulation systems
