AutoAgent: A Fully-Automated and Highly Self-Developing Framework that Enables Users to Create and Deploy LLM Agents through Natural Language Alone

From commercial processes to scientific studies, artificial intelligence factors can process huge data groups, simplify operations, and help decision -making. However, even with all these developments, the building and sewing of LLM agents remains an arduous task for most users. The main reason is that artificial intelligence agent platforms require programming skills, restricting access to just a small part of the population. With only 0.03 % of the world’s population have the necessary coding skills, the mass publication of LLM agents is out of the reach of non -technical users. Although artificial intelligence has become an increasingly essential tool in various industries, unlawful professionals cannot benefit from its full potential, and there is a big gap between technological ability and ease of use. One of the biggest problems in developing artificial intelligence agent is to rely on programming skills.

Current systems such as Langchain and Autogen specifically for developers who have a programming experience, complicating the design or sewing of artificial intelligence factors for non -technician individuals. This obstacle slows the use of artificial intelligence automation between people because most professionals do not have the technical capabilities needed to apply it. Despite the well -documented tools, the creation of Amnesty International Undersecretary usually requires sophisticated rapid engineering, API integration and error correction, making it out of reach of a wider audience. The problem is to create a system that does not require coding but still provides users with flexible and strong AI work.

The current frameworks mostly work within the environments directed to developers, and require experience in deep programming. Langchain, for example, is widely used to create the LLM application but requires prior knowledge of API calls and processing data processing. Other options, such as Autogen and Camel, increase LLM functions by allowing factors to interact with each other based on roles. However, it also depends on technical settings that may be difficult for non -technical users to implement. Although the tools have made artificial intelligence automation better, they remain unable in most cases for non -encrypted users. The lack of a truly zero solution has led to limited access to artificial intelligence, which prevents broader dependence among non -developers.

Researchers from Hong Kong University presented AutoagentThe fully and zero automatic AI AIS framework designed to fill this gap. Autoagent enables users to create and publish LLM agents using natural language orders, eliminating the need for programming experience. Unlike current solutions, spontaneity works as a self -development worker, as users describe tasks in a clear language and independently generate factors and workflow. The frame includes four main components: agent aid tools, a llm -backed enforceable engine, self -management file system, and self -playing unit. These components allow users to create an AI dependent solutions for different applications without writing one line of code. Autoagent aims to add a democratic character to the development of AI, which makes smart automation within reach a broader audience.

The automatic work framework works through an advanced multi -agent structure. In essence, the executive engine in which LLM operates natural language instructions into organized workflow. Unlike the traditional frameworks that require manual coding, Autoagent automatically builds artificial intelligence factors based on the insertion of the user. The self -file system enables effective data processing by automatically converting various file formats into research rules. This ensures that artificial intelligence agents can recover relevant information through multiple sources. The self -playing customer customization unit enhances the ability to adapt by improving the functions of the worker frequently. These automatic components allow the complex tasks that AI drives without human intervention. This approach greatly reduces the complexity of the development of the artificial intelligence factor, which makes it accessible to non -programming while maintaining high efficiency.

The automatic performance evaluation showed significant improvements on the current frameworks. She got the second highest rating on Gaia Benchmark, a strict assessment of artificial aides, with a total accuracy of 55.15 %. At level 1, Autoagent achieved a resolution of 71.7 %, outperforing open source work frames such as Agent LangFun (60.38 %) and Friday (45.28 %). The effectiveness of the system in the generation of retrieval (RAG) was also noticeable. On the standard of multiple desire, Autoagent made a resolution of 73.51 %, outperformed Langchain (62.83 %) while maintaining a much less 14.2 % error rate. Autoagent showed the ability to adapt to the complex multiple agents, the performance exceeds models such as Magentic-1 and OMNE in solving organized problems.

The search for Autoagent offers many fast food that highlights its impact and developments in artificial automation:

1. Autoagent eliminates the need for programming experience, allowing users to create LLM agents and publish it with natural language orders.
2. Autoagent ranked second in Gaia, with a resolution of 71.7 % in level 1 and outperforming many existing frameworks.
3. Autoagent achieved a resolution of 73.51 % on the standard of multiple desire, indicating improving recovery and thinking capabilities.
4. The system generates workflow dynamics and performs artificial intelligence factors, allowing the solution to the most efficient problems in complex tasks.
5. Autoagent successfully automate financial analysis, managing documents and other realistic applications, and displaying their diversity.
6. By making the creation of an LLM agent available to non -technical users, it automatically expands AI’s use beyond software engineers and researchers.
7. The autonomous file system allows smooth integration of data, ensuring that artificial intelligence agents can recover and process information efficiently.
8. The self -playing agent customization unit improves the performance of the agent through repetitive learning, which reduces manual intervention.

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