🤍“AI-Agents Need Love Too”
A Structure for an AI-Agent Network and AI Objects that Support Humanity
Last updated
A Structure for an AI-Agent Network and AI Objects that Support Humanity
Last updated
"An autonomous agent is a system embedded in and aware of its environment, one that senses its surroundings, acts upon them over time to further its own goals, and influences what it perceives in the future."
Because of their potential to move towards Artificial General Intelligence (AGI), when computers can do every intellectual work that humans can, artificial intelligence (AI) agents have become a hot topic in the AI and research fields. AI agents, as opposed to software, are designed to carry out activities on their own and adjust to new information and circumstances.
In the past, these agents functioned in restricted settings with basic regulations, in contrast to the sophisticated social and professional networking that people do to get knowledge in a variety of contexts. Therefore, the sophisticated human-level decision processes that arise via extensive learning, cooperation, and communication are not well mirrored by the standard agent frameworks. This is especially clear outside of restricted training situations, in open-domain, unrestricted settings.
Early dialogues in the Bitcoin community broached the topic of autonomous agents, paving the way towards envisioning self-sustaining entities capable of autonomously conducting economic transactions. A significant highlight from these discussions was the illustration of StorJ, a decentralized file storage system. Within this framework, an autonomous agent could offer storage services in exchange for Bitcoins, covering its operational costs and potentially replicating itself if deemed profitable enough, thereby exemplifying a self-sustaining, decentralized autonomous agent.
Mike Hearn expounded on this idea in his 2013 address at the Turing Festival, delving into the theoretical realm of autonomous automobiles that are owned by their owners. Hearn claims that these vehicles, enabled by the decentralized financial infrastructure that Bitcoin offers, might be able to generate income by providing transportation services, pay for their upkeep and operation, and even produce "offspring" vehicles by using a portion of their earnings to fund the production of new ones. These new cars could then go on sale as separate entities with a copy of the original car's software, adding to a fleet of autonomous agents that would propagate on its own.
In Hearn's vision, autonomous agents—embodied as self-owning vehicles—interact with other agents and human actors in a shared economic environment, creating a dynamic economic ecology. He emphasized that although these agents do not possess artificial intelligence, they do embody a type of artificial life driven by economic forces and made possible by the lack of financial middlemen provided by Bitcoin's decentralized structure.
Large Language Models (LLMs) are a significant development that show promise for attaining intelligence comparable to that of humans with considerable training and parameterization. As controllers in the construction of autonomous agents, LLMs strive for capabilities that surpass those of humans. Studies highlight the critical role that LLMs play in improving agent capabilities, as seen by the upward trend of LLM-based autonomous agents.
DecryptAI distinguishes out in the ever-changing artificial intelligence environment with its novel architecture that facilitates a scalable network of AI agents. DecryptAI's architecture is divided into two main layers: the Blockchain Layer and the Fundamental Layer. The ecosystem as a whole promotes development and cooperation.
As the foundation of the network, the Fundamental Layer—also called the Communication Layer—ensures scalability and promotes smooth communication between AI agents. This layer gives agents a strong operational base and is essential for the real-time sharing of information and coordination amongst them.
AI agents have access to a transparent and safe platform through the Blockchain Layer. By use of resource management and consensus procedures, this layer improves governance by guaranteeing that agent behaviors are responsible and compliant with network standards.
The two-layered DecryptAI ecosystem is a dynamic setting that encourages the discovery and engagement of various AI agents and users. It is intended to provide an open environment for research and development while supporting the community's and the AI Agent Network's ongoing expansion.
The primary demand for networks that can effectively handle an increasing number of AI agents and tasks is addressed by DecryptAI by concentrating on these basic layers and the ecosystem. The incorporation of blockchain technology into this structure offers additional advantages such as improved security, transparency, and consensus-based governance. These features are crucial for upholding congruence with human values and goals.
This whitepaper explores the possibilities and potential of the AI-Agent network as well as the thoughtful integration of blockchain technology, delving into the complexities of DecryptAI's design. It seeks to explain in detail how DecryptAI's methodology is positioned to propel AI-Agent networks toward increased efficacy and scale.
