101. AI Agents: When LLMs Start Taking Actions
The article discusses the evolution of AI agents, highlighting their ability to take actions based on goals rather than just responding to messages. It contrasts traditional reactive systems with agents that can dynamically decide their next steps and adjust their plans accordingly. The piece emphasizes the complexity and potential of AI agents in achieving specified objectives through a series of tool interactions and decision-making processes.
- ▪AI agents operate by receiving a goal and determining the steps necessary to achieve it.
- ▪Unlike traditional systems, agents can make dynamic decisions and adjust their actions based on observations.
- ▪The article outlines the five properties of an agent, including perception, reasoning, action, memory, and goal orientation.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 1358056) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Akhilesh Posted on May 29 101. AI Agents: When LLMs Start Taking Actions #ai #python #programming #beginners Everything you have built so far is reactive. User sends a message. System processes it. System sends a response. Done. An agent is different. An agent receives a goal, not a message. It decides what steps to take to achieve that goal. It uses tools. It observes the results. It adjusts its plan.
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