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 immense economic potential for those who leverage intelligent agents to enhance their financial behaviors. AI agents are a class of autonomous tools equipped with data analysis, decision-making, and trading execution capabilities, operating with varying degrees of human involvement. Currently, these agent tools are becoming accessible to the public, gradually disrupting the 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 reviewing the automated agent projects that have already launched and focus on providing services to individual users. To this end, the project team conducted extensive research and interviews with dozens of teams in the industry, ultimately compiling a rigorously selected list of active projects, categorized by product type, with annotations for each representative product.
Agentic Finance is driving the cryptocurrency industry towards maturity, providing real-time information, professional-level advice, and optimizing user experiences, making ordinary users' participation in DeFi more efficient and reliable. Below is a structured overview of the current ecosystem:
What is Agentic Finance (AgentFi)?
Agentic Finance refers to an emerging category of financial products that focuses on actively managing user funds using AI or machine learning, or providing personalized financial advice. Some products achieve interaction and analysis through large language models (LLMs), while others rely on rule engines or traditional machine learning algorithms. Despite varying underlying technology 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 adoption curve. Soon, various agents and AI assistants will dominate financial activities. Source: Ramp
However, it is foreseeable that in the near future, traders, asset managers, financial analysts, and other professionals will enhance their efficiency using dedicated intelligent agent tools, while automated agent versions targeting ordinary users will also be launched simultaneously. This trend has already begun to manifest: for example, on the Solana network, automated trading bots now account for over half of the trading volume.
Autonomy vs Intelligence: The capability coordinate system of AgentFi
Different Agentic projects are distributed across 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 tools based on rules and statistical models, the middle consists of traditional machine learning models, and the right side consists of advanced agents based on large language models (LLMs) or subsequent technologies;
The vertical axis represents the degree of autonomy: the bottom represents 'advisory agents' that only provide suggestions and analyses, the top represents 'fully automated agents' with complete decision-making and execution authority, and the middle represents a hybrid architecture of 'Human-in-the-loop'.
When mentioning Agentic Finance, many people think of 'invisible robots' or advanced LLM systems that can trade automatically and independently manage portfolios. However, in reality, such systems have not yet been deployed on a large scale, mainly due to the instability issues still present in LLMs. For instance, LLMs can still 'hallucinate' false information and only recently gained basic counting capabilities (like counting how many letter 'r's are in 'strawberry'). Currently, most agents only use LLMs for human-computer interaction interfaces or data analysis layers, while the asset 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 trajectory of LLMs, their weaknesses in handling numbers and logical reasoning have historical reasons—they were initially designed for language prediction. But this situation is rapidly changing. For example, Anthropic has launched financial products adopted by institutions, and OpenAI has trained models that are competitive in the International Mathematical Olympiad.
2025 Landscape of Agentic Finance Projects
The following is a list of currently launched Agentic projects that have asset management capabilities and are open to users. Projects in development or internal testing phases are not included, and products that only use LLMs as interfaces but require user manual decision-making are also excluded, leading to many projects not being included in this round of review.
Trading and asset allocation agents
Trading agents are the most commonly thought-of agent-based financial products. These agents manage user funds by automatically adjusting positions or selecting assets to buy and sell. To achieve automated trading, agent systems typically need components such as trading permissions, asset access, budget management, preset strategies, and high-quality data. Below is a list of current projects supporting one or more of these functions:
According to a recent poll initiated by Cambrian on the X platform, most users show a high interest in high-risk trading agents.
Liquidity provision (LP) agents
Decentralized exchanges (DEX) rely on third-party liquidity providers (LP) to supply tradable assets, and the fees paid by traders are received by the LP. The earnings of LP depend 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 cryptocurrency 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. Below are some of the launched lending agent projects:
Prediction and betting agents
Prediction markets allow users to bet on the outcomes of future events, such as elections or sports events. These markets typically rely on real-time tracking of news or real-world information, which may change at any moment. Prediction markets are inherently aligned with agent-based participation mechanisms, which Vitalik Buterin also emphasized in his proposed concept of Information Finance (InfoFi).
Sentiment, fundamentals, news, and technical analysis agents
Investors typically rely on market analysis to determine 'what to buy' and use sentiment analysis to judge 'when to buy or sell'. LLMs demonstrate transformative value in this type of analysis: not only significantly expanding the scale and speed of analyzable data but also enhancing contextual understanding to provide more comprehensive insights by recognizing correlations between data sources. Unlike the executable trading agents mentioned above, analytical agents only provide informational support and do not directly execute operations. Below are some representative projects in this category:
It is worth noting that the Agentic Finance ecosystem is evolving rapidly, and existing projects are continuously expanding their business boundaries. For example, products currently classified as lending agents may expand into liquidity management and other areas in the future.
Future trends of Agentic Finance
On-chain assets continue to grow, and on-chain stablecoin trading volumes have reached new highs, with traditional fintech companies also connecting to on-chain infrastructure. For example, Robinhood recently launched a tokenization service for US stocks, enabling 24/7 on-chain trading accessible to global investors.
The cryptocurrency industry is gradually surpassing the narrative of 'speculative trading' and moving towards broader application scenarios that encompass investment functions.
However, for many users, successfully participating in DeFi still presents a significant barrier. This is precisely where agent-based products come into play: they are expected to significantly enhance usability and profitability, becoming a key driver for the widespread adoption of DeFi.
Agentic Finance is an entirely new market segmentation, with the tools mentioned above being the first attempts in TradFi and DeFi. We expect that some early projects may fail to realize their vision, but the overall ecosystem will continue to mature. Ultimately, using agents will become the mainstream way to participate in finance, and those users who take the first step into 'Agentic Finance' early will be more likely to achieve long-term returns.
Moreover, as developers continue to deliver stable returns, user attention to the details of agent strategies will decrease, and in the future, agents may further integrate various capabilities (such as simultaneously managing trades and LP positions) to enhance complexity and efficiency.
Future areas of focus
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
About the author
Sam Green, founder of Cambrian Network, focuses on building a financial intelligence layer for agent systems. Previously, he was the co-founder and CTO of Semiotic Labs, leading AI and verifiability research at The Graph; he also participated in the development of the trading platform Odos (with a total trading volume of $100 billion and over 3 million users served). He served as an AI and cryptography researcher at Sandia National Laboratories, holds a master's degree in applied mathematics, and received a Ph.D. in computer science from the University of California, Santa Barbara.
About Cambrian
Cambrian is an on-chain financial intelligence layer for agents, with its API providing real-time and historical blockchain data to serve Agentic DeFi applications. The data covers yields, liquidity positions, risk exposures, whale dynamics, market sentiment, and standard DeFi metrics, aiming to provide verifiable financial insights.
For more information, please visit: https://www.cambrian.ai
This article is a submission and does not represent the views of BlockBeats