{"id":175,"date":"2026-09-17T16:48:59","date_gmt":"2026-09-17T19:48:59","guid":{"rendered":"https:\/\/blog.agenciabyte.com\/?p=175"},"modified":"2026-11-13T17:58:41","modified_gmt":"2026-11-13T20:58:41","slug":"jev-ia-modelo-decisoes-software","status":"publish","type":"post","link":"https:\/\/agenciabyte.com\/blog\/2026\/09\/17\/jev-ia-modelo-decisoes-software\/","title":{"rendered":"JEV IA: o modelo do co-criador do ChatGPT"},"content":{"rendered":"\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"450\" src=\"https:\/\/agenciabyte.com\/blog\/wp-content\/uploads\/2026\/09\/Captura-de-tela-2026-09-17-164242-1024x450.png\" alt=\"\" class=\"wp-image-176\" srcset=\"https:\/\/agenciabyte.com\/blog\/wp-content\/uploads\/2026\/09\/Captura-de-tela-2026-09-17-164242-1024x450-1.png 1024w, https:\/\/agenciabyte.com\/blog\/wp-content\/uploads\/2026\/09\/Captura-de-tela-2026-09-17-164242-300x132.png 300w, https:\/\/agenciabyte.com\/blog\/wp-content\/uploads\/2026\/09\/Captura-de-tela-2026-09-17-164242-767x337.png 767w, https:\/\/agenciabyte.com\/blog\/wp-content\/uploads\/2026\/09\/Captura-de-tela-2026-09-17-164242.png 1049w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Se os LLMs tradicionais foram feitos para conversar, o <strong>JEV IA<\/strong> foi criado para <strong>decidir<\/strong><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p class=\"wp-block-paragraph\">Lan\u00e7ado em 15 de setembro de 2026 pela <strong>TypeSafe AI<\/strong>, startup do pesquisador <strong>Diogo Almeida<\/strong> (co-autor do InstructGPT e um dos nomes por tr\u00e1s do ChatGPT), o JEV \u00e9 o primeiro modelo da categoria <strong>\u201cSystem One\u201d<\/strong>: uma IA que n\u00e3o gera texto, mas devolve decis\u00f5es estruturadas e tipadas para serem usadas diretamente dentro de sistemas.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Para quem trabalha com automa\u00e7\u00e3o, integra\u00e7\u00f5es e agentes de IA, isso muda o jogo: em vez de depender de um chatbot para \u201cinterpretar\u201d e \u201cresponder\u201d, voc\u00ea passa a ter um <strong>motor de decis\u00e3o<\/strong> que classifica, escolhe e roteiriza com <strong>lat\u00eancia de milissegundos<\/strong> e custo drasticamente menor.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">O que \u00e9 o JEV IA e como ele funciona<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">O <strong><a href=\"https:\/\/typesafe.ai\/\">JEV IA<\/a><\/strong> n\u00e3o \u00e9 um chatbot. Ele recebe <strong>contexto + perguntas pr\u00e9-definidas<\/strong> e devolve <strong>op\u00e7\u00f5es escolhidas com probabilidades<\/strong>, tudo em formato estruturado (JSON), pronto para seu c\u00f3digo consumir.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Em termos simples:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Voc\u00ea define <strong>quais decis\u00f5es<\/strong> o modelo deve tomar (ex.: \u201cclassificar lead\u201d, \u201caprovar pedido\u201d, \u201croteirizar ticket\u201d).<\/li>\n\n\n\n<li>Informa as <strong>op\u00e7\u00f5es poss\u00edveis<\/strong> e um pequeno prompt por decis\u00e3o.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">O JEV retorna algo como:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>{\n  \"decisions\": &#91;\n    {\n      \"id\": \"classificar_lead\",\n      \"choice\": \"quente\",\n      \"confidence\": 0.87,\n      \"probabilities\": {\n        \"quente\": 0.87,\n        \"morno\": 0.11,\n        \"frio\": 0.02\n      }\n    }\n  ]\n}\/<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">Isso permite que seu sistema <strong>tome a\u00e7\u00f5es autom\u00e1ticas<\/strong> com base nessas decis\u00f5es, sem precisar interpretar texto livre.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A grosso modo ele vai te ajudar na etapa Tool Calling!<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Arquitetura System One do JEV IA<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A TypeSafe chama o JEV de <strong>System One Model<\/strong>, em contraste com os LLMs (\u201cSystem Two\u201d). A ideia \u00e9:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Focar em <strong>decis\u00f5es restritas<\/strong>, n\u00e3o em gera\u00e7\u00e3o aberta de linguagem.<\/li>\n\n\n\n<li>Usar <strong>amostragem paralela consciente de hardware<\/strong> para avaliar m\u00faltiplas hip\u00f3teses ao mesmo tempo.<\/li>\n\n\n\n<li>Treinar com <strong>Reinforcement Learning for Calibrated Decisions (RLCD)<\/strong>, buscando decis\u00f5es calibradas e menos \u201calucina\u00e7\u00f5es\u201d.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">O resultado, segundo a empresa, \u00e9 um modelo que roda <strong>20\u2013200\u00d7 mais r\u00e1pido<\/strong> e custa <strong>40\u2013400\u00d7 menos<\/strong> que LLMs de fronteira em tarefas de decis\u00e3o.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Desempenho e custo do JEV IA<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Dados oficiais do lan\u00e7amento (todos vindos da pr\u00f3pria TypeSafe; ainda sem auditoria independente ampla):<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Lat\u00eancia:<\/strong> entre <strong>70 e 500 ms<\/strong> por decis\u00e3o, dependendo da carga.<\/li>\n\n\n\n<li><strong>Pre\u00e7o:<\/strong>\n<ul class=\"wp-block-list\">\n<li><strong>US$ 0,042 por milh\u00e3o de tokens de entrada<\/strong><\/li>\n\n\n\n<li><strong>Sa\u00edda gratuita<\/strong> (n\u00e3o cobram tokens de resposta).<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Comparativos divulgados:<\/strong>\n<ul class=\"wp-block-list\">\n<li>At\u00e9 <strong>193,6\u00d7 mais r\u00e1pido<\/strong> e <strong>444,6\u00d7 mais barato<\/strong> que modelos conversacionais em certos fluxos testados pela pr\u00f3pria empresa.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Em termos pr\u00e1ticos, isso significa que voc\u00ea pode rodar <strong>milhares de decis\u00f5es por hora<\/strong> com custo muito baixo, viabilizando automa\u00e7\u00f5es em escala que antes eram proibitivas com LLMs tradicionais.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Casos de uso do JEV IA em automa\u00e7\u00e3o<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">O <strong>JEV IA<\/strong> brilha em cen\u00e1rios onde voc\u00ea j\u00e1 sabe <strong>quais decis\u00f5es<\/strong> precisa tomar e quer apenas <strong>mais intelig\u00eancia e calibra\u00e7\u00e3o<\/strong> nesse processo. Exemplos:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Classifica\u00e7\u00e3o de leads e oportunidades<\/strong>\n<ul class=\"wp-block-list\">\n<li>Decidir se um lead \u00e9 \u201cquente\u201d, \u201cmorno\u201d ou \u201cfrio\u201d com base em hist\u00f3rico, comportamento e dados do CRM.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Roteiriza\u00e7\u00e3o de tickets e solicita\u00e7\u00f5es<\/strong>\n<ul class=\"wp-block-list\">\n<li>Direcionar automaticamente chamados para o time\/fluxo correto (suporte, vendas, financeiro) com confian\u00e7a calibrada.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Aprova\u00e7\u00e3o autom\u00e1tica de pedidos e transa\u00e7\u00f5es<\/strong>\n<ul class=\"wp-block-list\">\n<li>Aprovar, revisar ou bloquear pedidos com base em regras + contexto (valor, cliente, hist\u00f3rico, risco).<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Triagem de documentos e formul\u00e1rios<\/strong>\n<ul class=\"wp-block-list\">\n<li>Classificar tipos de documento, identificar inconsist\u00eancias e decidir pr\u00f3ximos passos (validar, solicitar complemento, rejeitar).<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Agentes de IA com m\u00f3dulo de decis\u00e3o<\/strong>\n<ul class=\"wp-block-list\">\n<li>Usar o JEV como \u201cc\u00e9rebro decis\u00f3rio\u201d dentro de agentes mais complexos, enquanto outros modelos cuidam de linguagem e interface.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Se o seu processo hoje usa regras fixas (if\/else) ou depende de an\u00e1lise humana repetitiva, o <strong>JEV IA<\/strong> pode ser encaixado como uma <strong>camada de decis\u00e3o probabil\u00edstica<\/strong> entre seus dados e suas a\u00e7\u00f5es.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">JEV IA vs LLMs tradicionais<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th class=\"has-text-align-left\" data-align=\"left\">Crit\u00e9rio<\/th><th class=\"has-text-align-left\" data-align=\"left\">JEV IA (System One)<\/th><th class=\"has-text-align-left\" data-align=\"left\">LLM tradicional (GPT, Claude, etc.)<\/th><\/tr><\/thead><tbody><tr><td class=\"has-text-align-left\" data-align=\"left\">Sa\u00edda<\/td><td class=\"has-text-align-left\" data-align=\"left\">Decis\u00f5es tipadas + probabilidades (JSON)<\/td><td class=\"has-text-align-left\" data-align=\"left\">Texto livre, c\u00f3digo, conte\u00fado<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">Foco<\/td><td class=\"has-text-align-left\" data-align=\"left\">Decis\u00f5es dentro de software<\/td><td class=\"has-text-align-left\" data-align=\"left\">Conversa, cria\u00e7\u00e3o, racioc\u00ednio aberto<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">Lat\u00eancia t\u00edpica<\/td><td class=\"has-text-align-left\" data-align=\"left\">70\u2013500 ms<\/td><td class=\"has-text-align-left\" data-align=\"left\">Segundos (dependendo do modelo e tarefa)<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">Custo (ordem de grandeza)<\/td><td class=\"has-text-align-left\" data-align=\"left\">US$ 0,042 \/ milh\u00e3o tokens entrada<\/td><td class=\"has-text-align-left\" data-align=\"left\">US$ 0,20\u201310+ \/ milh\u00e3o tokens (varia muito)<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">Ideal para<\/td><td class=\"has-text-align-left\" data-align=\"left\">Automa\u00e7\u00e3o, triagem, classifica\u00e7\u00e3o, roteamento<\/td><td class=\"has-text-align-left\" data-align=\"left\">Chatbots, reda\u00e7\u00e3o, an\u00e1lise qualitativa, etc.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Na pr\u00e1tica, os dois s\u00e3o complementares: voc\u00ea pode usar um LLM para <strong>entender e estruturar<\/strong> a situa\u00e7\u00e3o e o <strong>JEV IA<\/strong> para <strong>decidir<\/strong> o que fazer a seguir.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Se os LLMs tradicionais foram feitos para conversar, o JEV IA foi criado para decidir Lan\u00e7ado em 15 de setembro de 2026 pela TypeSafe AI, startup do pesquisador Diogo Almeida (co-autor do InstructGPT e um dos nomes por tr\u00e1s do ChatGPT), o JEV \u00e9 o primeiro modelo da categoria \u201cSystem One\u201d: uma IA que n\u00e3o [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"byte_lang":"","byte_group":"","footnotes":""},"categories":[202],"tags":[69,23,104,34],"class_list":["post-175","post","type-post","status-publish","format-standard","hentry","category-inteligencia-artificial","tag-agentes-de-ia","tag-automacao-de-processos","tag-ia-para-empresas","tag-llm"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.8 - 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