Agentes de IA: cómo un LLM razona, usa herramientas y actúa solo
The article discusses the evolution of AI agents, highlighting their ability to reason, use tools, and act autonomously. Unlike traditional chatbots, these agents can pursue goals through a cycle of reasoning and action. The piece emphasizes the significance of this technology as a dominant trend for 2026, as noted by Gartner and IBM.
- ▪An AI agent is a program that uses a large language model (LLM) to make decisions and act on the world through tools.
- ▪The key difference between an AI agent and a traditional chatbot is autonomy, allowing the agent to pursue goals through multiple steps.
- ▪The most common technical pattern used by AI agents is called ReAct, which combines reasoning and acting.
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/lu1tr0n/agentes-de-ia-como-un-llm-razona-usa-herramientas-y-actua-solo-2od3 |
| Publication time | Wed, 03 Jun 2026 16:05:29 +0000 |
| Retrieval time | 2026-06-03T16:15:11.596Z |
| Last seen | 2026-06-03T16:15:11.596Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | C2XMhd4u1xSN |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 806044) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } lu1tr0n Posted on Jun 3 • Originally published at elsolitario.org Agentes de IA: cómo un LLM razona, usa herramientas y actúa solo #ai #machinelearning #programming #tutorial Durante años, la inteligencia artificial respondía una pregunta y se quedaba esperando la siguiente. En 2026 el patrón cambió: un agente de IA ya no se limita a contestar, sino que decide qué hacer, usa herramientas externas y repite el ciclo hasta cumplir un objetivo.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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