![\"8832f957jcwsfz005ed000iw007kp\"](\"https:\/\/www.1ai.net\/wp-content\/uploads\/2026\/04\/8832f957j00tcwsfz005ed000iw007kp.jpg\" "\\"8832f957jcwsfz005ed000iw007kp\\"")
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IN THE WORLD OF AIAgent<\/a>\uff08Agent<\/a>\u201cThe two years have almost become HF words. But it's funny -- a lot of people actually use Agent every day, but they don't realize they're using it\u3002<\/p>\n For example, you're writing code with Claude Code, Codex, Gemini CLI, OpenCode; or using Cursor, VSCode Plugin, Google Antigravity; or even, like OpenClaw, CoClaw, LobsterAI, such tools for automatic mission..<\/p>\n Behind these tools, they're actually running AI Agent\u3002<\/p>\n A lot of people would say, \"Oh, isn't that a smarter chat robot?\"<\/p>\n Not really\u3002<\/p>\n ChatBot can only answer questions, and Agent can think and act\u3002<\/p>\n Put it simply -- ChatBot is like a man who talks, Agent is more like a colleague who works for you\u3002<\/p>\n That's the real difference\u3002<\/p>\n The real AI Agent can not only talk, it can think, act, summarise and continue to think. If I compare AI Agent to one person:<\/p>\n I'm going to use the most popular metaphors to make things right\u3002<\/p>\n I. The four core parts of AI Agent<\/strong><\/p>\n 1 \ufe0f\u20e3 Model<\/p>\n The model is AI Agent's brain. It's like the human brain, responsible for:<\/p>\n A large model is an incompetent genius, while Agent encapsulates it as a software that can handle a task on its own through software development\u3002<\/p>\n Agent allows incompetent geniuses to act as callers\u3002<\/p>\n 2 \ufe0f\u20e3 Tools (actionable hands)<\/p>\n The tools are like the hands of AI Agent, so it can interact with the outside world. Common tools include:<\/p>\n Without tools, the model can only be \u201cthinking\u201d; with tools, it can be \u201cdoing\u201d\u3002<\/p>\n 3 \ufe0f\u20e3 Architectural layer (neurosystem planned)<\/p>\n It's like AI Agent's nervous system. It is responsible for:<\/p>\n In short, the hierarchy is the control centre that decides \u201cwhat to do next\u201d\u3002<\/p>\n 4 Deployment (stabilized body)<\/p>\n The deployment level is the \"body\" of AI Agent. It includes:<\/p>\n It ensures that AI Agent works steadily, unlike a temporary experimental script\u3002<\/p>\n How does AI Agent solve the problem<\/strong><\/p>\n AI Agent's problem-solving process is not a simple straight line of implementation, but a \"think-action-rethink\" cycle\u3002<\/p>\n Usually, AI Agent works like this:<\/p>\n Step 1: Receiving tasks<\/p>\n User questions, for example:<\/p>\n Step 2: Context of assembly<\/p>\n The system collates all relevant information into the model, including:<\/p>\n This is an important link that helps models understand the context of the problem\u3002<\/p>\n Step 3: Models to judge<\/p>\n And then the model thinks:<\/p>\n If the model felt that more information was needed, it might say:<\/p>\n Step 4: Implementation tools<\/p>\n The layer intercepts this tool call request and performs real operations such as calling a dedicated API, Web real-time query, execution code, execution script, etc\u3002<\/p>\n Once the results are available, the system will not be displayed immediately, but will continue ..<\/p>\n Step 5: Fill out the results and think again<\/p>\n The results of the implementation of the tool are added to the context and the model is rethought:<\/p>\n If not, it may continue to be supplemented by other tools\u3002<\/p>\n Step 6: Output final answer<\/p>\n When the model confirms that the information is sufficient, it produces the final answer\u3002<\/p>\n The whole process is like this:<\/p>\n Think, act, watch, think, think, think, act, output<\/p>\n That's AI Agent's closed ring mechanism\u3002<\/p>\n iii. Why is AI Agent better than a Prompt<\/p>\n The traditional approach is:<\/p>\n And AI Agent is:<\/p>\n The traditional approach focuses on optimizing a single hint, while AI Agent emphasizes the construction of an intelligent system that constantly adapts and optimizes the process to the context\u3002<\/p>\n This was the evolution from Prompt Engineering to Context Engineering\u3002<\/p>\n IV. A simple analogy<\/strong><\/p>\n Imagine, it's like:<\/p>\n The construction of AI Agent is more like:<\/p>\n YOU'RE NOT THE \"MAN WHO WRITES THE PROCESS\" ANYMORE, BUT YOU'RE THE \"DIRECTOR\" WHO LEADS AI TO THE MISSION\u3002<\/p>\n V. CONCLUDING STATEMENT<\/strong><\/p>\n AI Agent is an intelligent system capable of performing tasks in a cycle by:<\/p>\n Not only can they think, they can do and they can be optimized\u3002<\/p>\n Therefore, it is no longer just a chat robot, but a \u201cdigital worker\u201d who can solve real problems\u3002<\/p>\n If the big model is the brain, then AI Agent is a complete, problem solver\u3002<\/p>\n The real challenge is not to make the model smarter, but to build a rational context and system of action that can efficiently replace our existing work\u3002<\/p>\n Okay, now do you understand what AI Agent is<\/p>","protected":false},"excerpt":{"rendered":" In the AI world, \u201cAgent\u201d has been almost a high-frequency word for two years. But it's funny -- a lot of people actually use Agent every day, but they don't realize they use it. For example, you're writing code with Claude Code, Codex, Gemini CLI, OpenCode; or using Cursor, VSCode Plugin, Google Antigravity; or even, like OpenClaw, CoClaw, LobsterAI, such tools for automatic mission... Behind these tools, they're actually running AI Agent. A lot of people would say, \"Oh, isn't that a smarter chat robot?\"<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[144],"tags":[1405,1355],"collection":[],"class_list":["post-51861","post","type-post","status-publish","format-standard","hentry","category-baike","tag-agent","tag-1355"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts\/51861","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/comments?post=51861"}],"version-history":[{"count":0,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts\/51861\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/media?parent=51861"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/categories?post=51861"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/tags?post=51861"},{"taxonomy":"collection","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/collection?post=51861"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n
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