{"id":2038,"date":"2025-04-04T11:04:13","date_gmt":"2025-04-04T11:04:13","guid":{"rendered":"https:\/\/elink.cat\/blog\/?p=2038"},"modified":"2025-06-18T14:10:30","modified_gmt":"2025-06-18T14:10:30","slug":"com-funciona-un-agent-dia-arquitectura-i-components-clau","status":"publish","type":"post","link":"https:\/\/elink.cat\/blog\/com-funciona-un-agent-dia-arquitectura-i-components-clau\/","title":{"rendered":"Com funciona un agent d\u2019IA? Arquitectura i components clau"},"content":{"rendered":"Temps de lectura: <\/span> 2<\/span> minuts<\/span><\/span>

 <\/p>\n

Els agents d\u2019intel\u00b7lig\u00e8ncia artificial<\/b><\/span> no s\u00f3n nom\u00e9s interf\u00edcies intel\u00b7ligents que responen preguntes. S\u00f3n sistemes aut\u00f2noms capa\u00e7os de raonar, decidir i actuar<\/b><\/span>. Per\u00f2\u2026 com estan constru\u00efts realment? Quins components fan possible que funcionin?<\/p>\n

Analitzarem l\u2019arquitectura b\u00e0sica d\u2019un agent d\u2019IA<\/b><\/span>, posant especial \u00e8mfasi en els agents moderns basats en models de llenguatge (LLMs)<\/b><\/span> i les plataformes que els donen suport.<\/p>\n

Parlarem dels blocs b\u00e0sics, el bucle de funcionament, les arquitectures m\u00e9s populars i les eines que pot fer servir un agent.<\/p>\n

Els blocs b\u00e0sics d\u2019un agent<\/b><\/b><\/h4>\n

Tot agent funcional sol estar format per quatre grans components:<\/p>\n

1. L’observaci\u00f3 o percepci\u00f3<\/b><\/h5>\n

<\/b><\/b>\u00c9s la capacitat de recollir informaci\u00f3<\/b><\/span> del m\u00f3n exterior. Pot venir de:<\/p>\n

    \n
  • \n
      \n
    • Entrades de text (converses amb usuaris…)<\/li>\n
    • Dades d\u2019un sistema (APIs, fitxers, sensors)<\/li>\n
    • Resultats d\u2019eines (navegaci\u00f3, cerca, c\u00e0lculs)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n
      2. El raonament i presa de decisions<\/b><\/b><\/h5>\n

      Aqu\u00ed \u00e9s on entra en joc la intel\u00b7lig\u00e8ncia:<\/p>\n

        \n
      • \n
          \n
        • LLMs com GPT o Claude<\/b><\/span> processen les dades i avaluen opcions.<\/li>\n
        • L\u2019agent decideix qu\u00e8 fer a continuaci\u00f3<\/b><\/span> (resoldre, buscar, preguntar m\u00e9s\u2026).<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n
          3. L’acci\u00f3<\/b><\/b><\/h5>\n

          El sistema actua segons el pla:<\/p>\n

            \n
          1. \n
              \n
            1. Crida una eina externa (una API, un buscador, un codi).<\/li>\n
            2. Escriu una resposta.<\/li>\n
            3. Executa una acci\u00f3 en una interf\u00edcie (automatitzaci\u00f3).<\/li>\n<\/ol>\n<\/li>\n<\/ol>\n
              4. La mem\u00f2ria<\/b><\/b><\/h5>\n

              Els agents poden tenir:<\/p>\n

                \n
              • \n
                  \n
                • Mem\u00f2ria a curt termini:<\/b><\/span> Mant\u00e9 el context de la conversa o acci\u00f3 actual.<\/li>\n
                • Mem\u00f2ria a llarg termini:<\/b><\/span> Recorda dades persistents (prefer\u00e8ncies, interaccions passades, informaci\u00f3 rellevant per a l\u2019usuari).<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n

                  \"\"<\/p>\n

                   <\/p>\n

                  El bucle de funcionament<\/b><\/b><\/h4>\n

                  Els agents segueixen un cicle iteratiu<\/b><\/span> que s\u2019anomena sovint Think \u2192 Act \u2192 Observe<\/i>:<\/p>\n

                    \n
                  1. Think:<\/b><\/span> Processen la informaci\u00f3 i decideixen qu\u00e8 fer.<\/li>\n
                  2. Act:<\/b><\/span> Fan una acci\u00f3 (busquen, pregunten, calculen\u2026).<\/li>\n
                  3. Observe:<\/b><\/span> Analitzen el resultat de l\u2019acci\u00f3 per decidir el seg\u00fcent pas.<\/li>\n<\/ol>\n

                    Aquest bucle es pot repetir diverses vegades fins que s\u2019arriba a una resposta final o a un objectiu complert.<\/p>\n

                    Arquitectures populars: LangChain, Auto-GPT i m\u00e9s<\/b><\/b><\/h4>\n

                    Hi ha plataformes que faciliten la construcci\u00f3 d\u2019aquests agents:<\/p>\n

                      \n
                    • LangChain:<\/b><\/span> Permet orquestrar LLMs amb eines, mem\u00f2ria i fluxos l\u00f2gics. Ideal per a agents conversacionals complexos.<\/li>\n
                    • Auto-GPT \/ AgentGPT:<\/b><\/span> Agents que s\u2019autogestionen per resoldre tasques amb m\u00ednim d’input<\/em> hum\u00e0.<\/li>\n
                    • CrewAI o MultiOn:<\/b><\/span> Orientades a la col\u00b7laboraci\u00f3 entre agents o l\u2019automatitzaci\u00f3 personal.<\/li>\n<\/ul>\n

                      Aquestes arquitectures combinen els components anteriors amb estrat\u00e8gies avan\u00e7ades de planificaci\u00f3, reflexi\u00f3 i \u00fas d\u2019eines.<\/p>\n

                      Quines eines pot fer servir un agent?<\/b><\/b><\/h4>\n

                      Els agents poden fer servir una varietat d\u2019eines externes:<\/p>\n

                        \n
                      • Cercadors (Google, DuckDuckGo, Bing)<\/li>\n
                      • Codificadors i int\u00e8rprets de codi (Python, JavaScript)<\/li>\n
                      • Bases de dades i APIs pr\u00f2pies d\u2019empresa<\/li>\n
                      • Calculadores, calendaris, traductors, etc.<\/b><\/li>\n<\/ul>\n

                        El fet que un agent pugui combinar la generaci\u00f3 de text<\/b><\/span> amb accions sobre entorns digitals<\/b><\/span> fa que esdevingui una pe\u00e7a clau en l\u2019automatitzaci\u00f3 intel\u00b7ligent. Un agent d\u2019IA \u00e9s molt m\u00e9s que un model de llenguatge: \u00e9s una estructura modular<\/b><\/span>, amb capacitats de percebre, raonar, actuar i aprendre. La seva arquitectura permet construir sistemes adaptatius, capa\u00e7os de treballar en entorns complexos i din\u00e0mics. A mesura que avancem cap a una IA m\u00e9s pr\u00e0ctica i aut\u00f2noma, entendre com funcionen per dins aquests agents<\/b><\/span> \u00e9s essencial per aprofitar tot el seu potencial. En seguir\u00e9 parlant en propers articles!<\/p>\n","protected":false},"excerpt":{"rendered":"

                        Temps de lectura: <\/span> 2<\/span> minuts<\/span><\/span>  Els agents d\u2019intel\u00b7lig\u00e8ncia artificial no s\u00f3n nom\u00e9s interf\u00edcies intel\u00b7ligents que responen preguntes. S\u00f3n sistemes aut\u00f2noms capa\u00e7os de raonar, decidir i actuar. Per\u00f2\u2026 com estan constru\u00efts realment? Quins components fan<\/p>\n","protected":false},"author":1,"featured_media":2049,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_crdt_document":"","breadcrumbs_single_post":"","page_title_panel":"","breadcrumbs_single_page":"","single_page_alignment":"","single_page_margin":"","page_structure_type":"","content_style_source":"","content_style":"","blog_post_streched_ed":"","blog_page_streched_ed":"","has_transparent_header":"","disable_transparent_header":"","vertical_spacing_source":"","content_area_spacing":"","single_post_content_background":"","single_page_content_background":"","single_post_boxed_content_spacing":"","single_page_boxed_content_spacing":"","single_post_content_boxed_radius":"","single_page_content_boxed_radius":"","disable_featured_image":"","disable_post_tags":"","disable_author_box":"","disable_posts_navigation":"","disable_comments":"","disable_related_posts":"","disable_header":"","disable_footer":"","footnotes":""},"categories":[31],"tags":[],"class_list":["post-2038","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-fonaments-dels-agents-dia","rishi-post"],"yoast_head":"\nCom funciona un agent d\u2019IA? 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