Perception (Perception): The core purpose of the perception module is to extend the AI agent’s perception space from a purely textual domain to a multimodal domain that includes text, audio, and visual patterns.
Action (Action): During the construction of the AI agent, the action module receives action sequences sent by the brain module and executes actions to interact with the environment.
Features of AI Agent Technology
Large models typically interact with users through prompts, with output effectiveness limited by the clarity of user queries. In terms of information processing, they only handle static or streaming data inputs without direct environmental interaction and cannot take actions autonomously. In technical applications, a lack of industry knowledge, susceptibility to hallucinations, and a high learning threshold for prompt engineering are obstacles to the large model’s expansion. In contrast, AI agents based on large models are designed to facilitate effective interactions with the environment by collecting environmental information through the perception module and altering environmental states via the action module. This integration of perception, decision-making, and action showcases advantages in autonomy, decision-making capabilities, and collaborative interaction, addressing the shortcomings of large models and establishing themselves as the “action-oriented” players in the AI field.
Depending on the target audience and processes, AI agents are primarily applied in three scenarios:
Single Agent Applications
In a specific environment, a single AI agent perceives, learns, and acts, needing to interact independently with the environment and optimize its behavior strategy based on environmental feedback to achieve preset goals. This can be applied in interactive scenarios such as game AI (e.g., Go, video games), autonomous vehicles, and robot control. The complexity of single-agent systems is relatively low, making them easier to implement and deploy in certain tasks.
Multi-Agent Systems
A complex distributed system composed of multiple intelligent agents (software programs, robots, or other autonomous entities), each possessing its own perception, decision-making, and action capabilities, and able to communicate, share information, interact, and collaborate with other agents to achieve common goals or tasks. Typically, the backend sets different roles for agents, while the frontend collaborates through dialogue chains to accomplish tasks that are difficult or impossible for a single agent, offering greater flexibility, scalability, and robustness. Applications include distributed control, intelligent transportation, smart manufacturing, and natural language processing.
AI Agent Platforms
Integrated platforms for constructing AI agent systems, where users define and deploy various agents. The platform optimizes agent combinations through strategic processes to meet specific task requirements, allowing agents to play different professional roles. After task negotiation and role assignment, they collaborate to execute tasks and integrate results. This is suitable for AI agent development and customized solutions for enterprises.
The evolution of mainstream AI agent products can be roughly divided into three stages based on time:
Framework Construction Stage
In March 2023, the AutoGPT framework project was released, comprising three core modules: task issuance, autonomous operation, and result output. Functionally, it primarily issues tasks to ChatGPT via prompts, which understands the semantic content through the large model, outputs detailed solutions, logically prioritizes steps to execute, generates executable actions or instructions, and calls external resources or tools to complete the instructions. The AutoGPT framework extrapolates the core capabilities of large models, such as natural language understanding, content generation, and logical reasoning, to specific scenarios, supplemented by perception and action technologies, showing potential for end-to-end problem-solving, and is regarded as an important model for the implementation of large models.
Prototyping Stage of GPTs
In November 2023, OpenAI launched the Assistant API and subsequently released the GPTs service, allowing users to build personalized custom GPT assistants without coding. By uploading personal data and custom training, users can rapidly construct vertical models, significantly lowering the creative threshold for AI applications and further boosting the AI agent trend.
Personal AI Agent Incubation Stage
In December 2023, Lenovo announced the progress of its personal AI agent “Xiao Le.” The personal AI agent is built on a local large model embedded in terminals, accurately understanding user intentions and converting them into corresponding task combinations, decomposing tasks, and identifying paths to task completion. It executes relevant tasks by querying local knowledge bases, calling device APIs, and appropriate models or applications, returning results to the agent, which integrates and feeds back to the user. Compared to cloud-based model capabilities, the entire process does not require cloud access, protecting user privacy while maintaining strong hardware control.
In the near future, AI agents will become the minimal working units of AI operating systems, with software embedded in autonomous agents likely to transform existing usage patterns from user-adaptive software to software-adaptive user habits, truly becoming personal assistants. Furthermore, system-level AI agents are expected to directly operate applications or sub-agents, with widespread application scenarios anticipated in PCs, mobile phones, and autonomous driving. Despite significant progress in large language model agents, they still face a series of technical challenges in practical applications, including security, ethics, computational resource consumption, complex tool usage, multi-agent interaction mechanisms, model adaptation methods, and real-world AI agent simulations.
[References][1] “Guidelines for the Construction of a Comprehensive Standardization System for the National Artificial Intelligence Industry (Draft for Comments),” Ministry of Industry and Information Technology, 2024[2] “2023 Comprehensive Survey on the Development and Application of AI Agents: Concepts, Principles, Development, Applications, Challenges, Prospects,” AI Frontier, 2023[3] “What is an AI Agent? What is the Difference Between AI Agents and Large Models? | ShopEx,” ShopEx, 2024, https://www.shopex.cn/news/archives/17685.html[4] “Achievements | Autonomous AI Agents Driven by Large Models and Collective Intelligence,” AIGC Frontline, 2024[5] “What is a Single Agent?” Industry Encyclopedia, 2024[6] “What is a Multi-Agent System?” Industry Encyclopedia, 2024[7] “Current Development Status, Industry Structure, and Trend Analysis of AI Agents,” Tianyi Think Tank, 2024[8] “AutoGPT: Principles and Practical Applications of Automated GPT,” Learning Ape, 2023[9] “Why Will ‘Agents’ Become the First Entry Point in the AI Era?” Geek Park, 2024[10] “Top Ten Cutting-Edge Technology Trends Report for 2023,” Quantum Bit Think Tank, 2023[11] “Large Language Models,” AIBOX, 2024
Reviewed by: Business Research Institute | Yang Lei
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