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Web3 infrastructure provider Fetch AI today announced the development of a suite of new trading tools for decentralized exchanges (DEXs). The tools use intelligent agent interactions to optimize DeFi trade execution and enhance the user experience.
The company’s proprietary platform is built on a decentralized network of agents, capable of performing a wide range of tasks, including data analysis, prediction and complex financial modeling.
In a major boost to the company’s expansion plans, it recently secured a whopping $40 million in funding from technology incubator and Web3 investment firm DWF Labs. In an official statement, Fetch AI said that it would use the investment to support the development and deployment of autonomous agents, network infrastructure and decentralized machine learning on its platform.
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The upcoming trading tools are the latest outcome of this funding, and are expected to officially launch in Q2 of 2023.
The DeFi market is predicted to reach $232 billion by 2030, with a compound annual growth rate of approximately 42%. However, inefficient trade execution and high fees have hindered the market’s growth.
Solving major UX problems in DeFi
Fetch AI’s upcoming tools utilize intelligent agents to execute trades on behalf of users, ensuring positive outcomes and reducing the need for manual intervention.
The agent-based trading model is a decentralized peer-to-peer approach that introduces automation and AI-based value capture to the DeFi realm.
Current DeFi solutions rely on smart contract-based liquidity pools and game-theoretic incentives. Agent-based trading replaces liquidity pools with agent-based matchmaking, and offers the end user agent-based end-to-end automation. Users need only to confirm transactions, as agents search, find, negotiate and transact on their behalf.
Agent-based trading also natively supports automation, such as limit orders and stop loss or other automated trading strategies, without compromising on decentralization or introducing complexity and cost by using other automation supporting networks. Users can code their trading strategies in their agents, which will tirelessly execute trades when the relevant market conditions are met.
In terms of security and privacy, blockchain networks’ inherent cryptographic primitives protect personal data. Transaction data is recorded on public, transparent ledgers but is anonymized, and no personally identifiable data is revealed. Agents have their own public-private key pair and exist separately from wallets, with cryptographic security measures in place.
Fetch AI’s agent-based trading tools also mitigate the risks of hacks and rugpulls by removing liquidity pools, which are highly targeted and often capital-inefficient. Instead, agents use decentralized solutions. Additional security measures such as cryptographic primitives and escrow-based smart contracts are also in place.
Finally, the agent-based technology supports interoperability and collaboration among different decentralized applications and networks. The agents are capable of appending transactions between all other IBC-enabled chains, using the Cosmos SDK in combination with the IBC protocol. The Cosmos ecosystem inherently provides significant degrees of interoperability, and agents are interoperable by default. Additionally, the technology can be exported to other chains through plugins.
According to Fetch AI CEO Humayun Sheikh, adoption of DeFi can be encouraged by introducing robust peer-to-peer trading through replacing liquidity pools with agent-based connectivity. This approach can significantly decrease operational risk while introducing time-saving automation.
Sheikh also noted that the platform’s underlying code base is expected to be released as open source, which will allow developers to build upon the platform’s components and create increasingly secure and decentralized DeFi environments. The system is designed to provide a flexible and extensible foundation for the creation of agent-based DEX and customized trading solutions.
The road ahead
“Fetch AI is in a unique position to enable machine-to-machine DeFi,” said Sheikh. “Due to agents’ ability to represent not only people and organizations but objects as well, we can imagine a future where machines of all sorts trade resources among each other, learning as they go.” He believes that the increasing tokenization of resources will lead to a growing number of agent-based value transfers.
He added that since agents can represent objects, a future can be imagined where agents representing cars, for example, can trade tokenized parking spots in dense urban clusters to optimize parking availability and monetization.
As markets grow in scope, size and complexity, tools to navigate these evolving markets will be needed. On the one hand, Fetch AI’s network acts as an execution layer to facilitate agent-based transaction processing. On the other hand, the developer tools and frameworks the company has created allow other people, projects and companies to easily create robust, secure, customizable agent-based solutions that most aptly fit their specific needs.
“We are in the midst of a seismic shift from the boom of generative AI spearheaded by the use of LLMs from the likes of companies such as OpenAI and Google. Increasingly, use of generative AI in existing applications or yet unimagined new applications will become a pervasive trend. With this broader context in mind, Fetch AI’s agent tech will help bring these AI capabilities to DeFi and the broader Web3 ecosystem,” said Sheikh.