The ecological landscape of Agentic Finance in 2025

Written by: 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

It is expected that the overall ecosystem will continue to mature, and the use of agents will ultimately become the mainstream way of financial participation.

Agentic Finance is approaching a critical tipping point, holding immense economic potential for those who leverage smart agents to enhance their financial behaviors. AI agents are a class of autonomous tools equipped with data analysis, decision-making, and trade execution capabilities, operating with varying degrees of human involvement. Currently, these agent tools are becoming accessible to the public, gradually disrupting the financial system that has long been dominated by Wall Street and its high-frequency algorithms.

This article focuses on the retail application of agent-based finance in “Decentralized Finance (DeFi)” and comprehensively organizes the automated agent projects that have been launched and are focused 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.

Agency-based finance is driving the cryptocurrency industry towards maturity, providing real-time information, professional-level advice, and optimizing user experience, 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 primarily focuses on actively managing user funds using AI or machine learning, or providing personalized financial advice. Some products leverage large language models (LLMs) for interaction and analysis, while others rely on rule engines or traditional machine learning algorithms. Despite 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.

However, it is foreseeable that in the near future, professional practitioners such as traders, asset managers, and financial analysts will use dedicated intelligent agent tools to improve efficiency. At the same time, automated agent versions aimed at ordinary users will also be launched simultaneously. This trend has already begun to emerge: for example, on the Solana network, automated trading bots now account for over half of the trading volume¹.

Autonomy vs Intelligence: The Capability Coordinate System of AgentFi

Different Agentic projects are distributed within the “autonomy - intelligence” coordinate system based on their service scenarios and technical capabilities.

The horizontal axis represents the level of intelligence: on the left are tools based on rules and statistical models, in the middle are traditional machine learning models, and on the right are advanced agents based on large language models (LLM) or their subsequent technologies;

The vertical axis represents the degree of autonomy: at the bottom is the “Advisory Agent” which only provides suggestions and analysis, at the top is the “Fully Automated Agent” with complete decision-making and execution authority, and in the middle is the “Human-in-the-loop” hybrid architecture.

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 issues with LLM stability. For instance, LLMs can still “hallucinate” false information and only recently acquired basic counting abilities (such as counting how many letter r’s are in strawberry). Currently, most agents use LLMs primarily for human-computer interaction interfaces or data analysis layers, while the funds management aspect still largely relies on mature statistical models or machine learning algorithms, technologies that have been used in the traditional finance (TradFi) sector for decades.

From the development path of LLM, its weaknesses in handling numbers and logical reasoning have historical reasons—it was originally designed for language prediction. However, this situation is rapidly changing. For example, Anthropic has launched financial products adopted by institutions, while OpenAI has trained models that are competitive in the International Mathematical Olympiad.

2025 Agentic Finance Project Overview

The following is a list of Agentic projects that are currently live, have fund management capabilities, and are open to users. Projects that are in development or internal testing are not included, and products that only use LLM as an interface but require manual decision-making by users are also excluded. Therefore, many projects have not been included in this round of review.

Trading and asset allocation agents

Trading agents are the most commonly thought of proxy financial products by the public. These agents manage user funds by automatically adjusting positions or selecting buy and sell assets. To achieve automated trading, the agent system typically needs to have components such as trading permissions, asset access, budget management, preset strategies, and high-quality data. Below is a list of projects currently supporting one or more of these functions:

According to a recent poll initiated by Cambrian on platform X, the majority of users have shown a high interest in high-risk trading agents.

Liquidity Provider (LP) type agent

Decentralized exchanges (DEX) rely on third-party liquidity providers (LP) to provide tradable assets, and the fees paid by traders are earned by the LPs. The earnings of LPs depend on various factors, including impermanent loss, trading volume, DEX protocol incentives, and more. The following proxy tools can help LPs identify the optimal liquidity allocation paths:

Lending Agent

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 lending agent projects that have been launched:

Prediction and betting agency

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 can change at any moment. Prediction markets are naturally suited to an agent-based participation mechanism, which is also emphasized by Vitalik Buterin in his proposed concept of InfoFi.

Emotional, fundamental, news, technical analysis agent

Investors typically rely on market analysis to determine “what to buy” and use sentiment analysis to decide “when to buy and sell.” LLM demonstrates transformative value in such analyses: it not only significantly expands the scale and speed of analyzable data but also enhances contextual understanding, providing more comprehensive insights by identifying the connections between data sources. Unlike the executable transaction agents mentioned above, analytical agents only provide informational support and do not execute operations directly. Below are some representative projects among them:

It is worth noting that the Agentic Finance ecosystem is rapidly evolving, and existing projects are continuously expanding their business boundaries. For example, products currently classified as lending agents may expand into areas such as liquidity management in the future.

The Future Trends of Agentic Finance

On-chain assets continue to grow, 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 tokenization services for U.S. stocks, enabling 24/7 on-chain trading available to global investors.

The cryptocurrency industry is gradually moving beyond the narrative of “speculative trading” and towards a broader application scenario that includes investment functions.

However, for many users, there still exists a considerable barrier to successfully participating in DeFi. This is precisely the entry point for proxy products: it is expected to significantly enhance usability and profitability, becoming the key to promoting the widespread adoption of DeFi.

Agentic Finance is a brand new market segment, and the tools mentioned above are the first attempts in both TradFi and DeFi. We anticipate that some of the early projects may not realize their visions, but the overall ecosystem will continue to mature. Ultimately, using agents will become the mainstream way of financial participation, and those users who take the first step into “agentic finance” early on will be more likely to reap long-term rewards.

In addition, as developers continue to deliver stable returns, users’ attention to the details of proxy strategies will decrease. In the future, proxies may further integrate multiple capabilities (such as simultaneously managing trades and LP positions) to enhance complexity and efficiency.