{"id":14542,"date":"2025-08-20T18:22:42","date_gmt":"2025-08-20T18:22:42","guid":{"rendered":"https:\/\/cheesecakelabs.com\/blog\/ai-agents-vs-ai-systems-arquitetura\/"},"modified":"2026-08-15T06:56:22","modified_gmt":"2026-08-15T06:56:22","slug":"ai-agents-vs-ai-systems-arquitetura","status":"publish","type":"post","link":"https:\/\/cheesecakelabs.com\/blog\/br\/ai-agents-vs-ai-systems-arquitetura\/","title":{"rendered":"AI Agents vs AI Systems: o que muda na arquitetura de software"},"content":{"rendered":"\n<p>As aplica\u00e7\u00f5es de IA passam por uma transforma\u00e7\u00e3o estrutural. O padr\u00e3o antigo eram <strong>AI Systems<\/strong> est\u00e1ticos e movidos a pipeline, como motores de recomenda\u00e7\u00e3o ou classificadores. O que se v\u00ea agora \u00e9 a mudan\u00e7a para <strong>AI Agents<\/strong>: entidades din\u00e2micas e aut\u00f4nomas, capazes de perceber, raciocinar e agir com base no contexto do mundo real.<\/p>\n\n\n\n<p>Essa mudan\u00e7a espelha o que acontece na arquitetura de software: de workflows previs\u00edveis para loops de orquestra\u00e7\u00e3o aut\u00f4nomos. A seguir, as <strong>diferen\u00e7as de arquitetura e de implementa\u00e7\u00e3o<\/strong> entre os dois paradigmas, e o que significa construir sistemas de fato inteligentes.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Diferen\u00e7as entre AI Systems e AI Agents<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><\/td><td><strong>AI Systems<\/strong><\/td><td><strong>AI Agents<\/strong><\/td><\/tr><tr><td><strong>Objetivo<\/strong><\/td><td><strong>Automa\u00e7\u00e3o de tarefas espec\u00edficas<\/strong><br>(ex.: chatbots, recomenda\u00e7\u00f5es)<\/td><td><strong>Resolu\u00e7\u00e3o aut\u00f4noma de problemas<\/strong><strong> <\/strong>(ex.: agendamento, negocia\u00e7\u00e3o) para atingir objetivos<\/td><\/tr><tr><td><strong>Autonomia<\/strong><\/td><td><strong>Baixa<\/strong> <br>Exige prompts e inputs expl\u00edcitos<\/td><td><strong>Baixa \u2192 Alta<\/strong><br>Decide com uma quantidade configur\u00e1vel de input humano<\/td><\/tr><tr><td><strong>Aprendizado<\/strong><\/td><td><strong>Passivo<\/strong> <br>Melhora com feedback do usu\u00e1rio e retreinamento&nbsp; (ex.: testes A\/B)<\/td><td><strong>Ativo<\/strong><br>Melhora sozinho a partir das intera\u00e7\u00f5es com o ambiente<\/td><\/tr><tr><td><strong>Intera\u00e7\u00e3o<\/strong><\/td><td><strong>Pergunta e resposta<\/strong> <br>Busca informa\u00e7\u00e3o em fontes de dados definidas<\/td><td><strong>Orientada a objetivo<\/strong><br>Executa tarefas (chamadas de API, edi\u00e7\u00f5es)<\/td><\/tr><tr><td><strong>Casos de uso<\/strong><\/td><td>\u2022 Bots de atendimento ao cliente<br>\u2022 Motores de recomenda\u00e7\u00e3o<\/td><td>\u2022 Cadeia de suprimentos aut\u00f4noma<br>\u2022 Assistentes pessoais<\/td><\/tr><tr><td><strong>Exemplo<\/strong><\/td><td>Algoritmo de recomenda\u00e7\u00e3o da <strong>Netflix<\/strong><\/td><td>Otimiza\u00e7\u00e3o de rotas de entrega da <strong>Amazon<\/strong><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">As diferen\u00e7as centrais de arquitetura<\/h3>\n\n\n\n<p>Sistemas de IA tradicionais t\u00eam \u00f3timo desempenho em tarefas espec\u00edficas, mas s\u00e3o est\u00e1ticos: n\u00e3o mudam de comportamento diante de um contexto novo.<\/p>\n\n\n\n<p>Agents, por outro lado, evoluem com os dados, aprendem com feedback e escolhem ativamente como resolver problemas.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Caracter\u00edstica<\/strong><\/td><td><strong>AI Systems (ML tradicional \/ LLM em passada \u00fanica)<\/strong><\/td><td><strong>AI Agents<\/strong><\/td><\/tr><tr><td><strong>Tipo de arquitetura<\/strong><\/td><td>Pipeline ou microsservi\u00e7o<\/td><td>Framework de agent modular, baseado em loop<\/td><\/tr><tr><td><strong>Estado<\/strong><\/td><td>Sem estado<\/td><td>Com estado<\/td><\/tr><tr><td><strong>Fluxo de controle<\/strong><\/td><td>Linear, manual<\/td><td>Din\u00e2mico, guiado por feedback<\/td><\/tr><tr><td><strong>Mem\u00f3ria<\/strong><\/td><td>Nenhuma \/ contexto limitado ao input<\/td><td>Mem\u00f3ria persistente e em evolu\u00e7\u00e3o (ex.: bancos vetoriais)<\/td><\/tr><tr><td><strong>Uso de ferramentas<\/strong><\/td><td>Fun\u00e7\u00f5es fixas ou nenhuma integra\u00e7\u00e3o<\/td><td>Uso adaptativo de ferramentas via APIs e plugins<\/td><\/tr><tr><td><strong>Autonomia<\/strong><\/td><td>Guiada por tarefa<\/td><td>Guiada por objetivo<\/td><\/tr><tr><td><strong>Integra\u00e7\u00e3o<\/strong><\/td><td>Endpoints de API ou servi\u00e7os embarcados<\/td><td>Orquestra\u00e7\u00e3o ciente de contexto (MCP, ferramentas)<\/td><\/tr><tr><td><strong>Exemplos<\/strong><\/td><td>Motores de recomenda\u00e7\u00e3o, detec\u00e7\u00e3o de fraude, tradu\u00e7\u00e3o<\/td><td>Assistentes de pesquisa, sistemas RAG aut\u00f4nomos, bots de workflow<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Implementa\u00e7\u00e3o de AI Systems<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Arquitetura t\u00edpica<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Entrada: input estruturado (features do usu\u00e1rio, dados de sess\u00e3o, texto)<\/li>\n\n\n\n<li>Processamento:\n<ul class=\"wp-block-list\">\n<li>Modelos cl\u00e1ssicos de ML (ex.: XGBoost, <a href=\"https:\/\/cheesecakelabs.com\/blog\/regressao-em-ia-tipos-e-aplicacoes\/\" target=\"_blank\" rel=\"noreferrer noopener\">regress\u00e3o<\/a> log\u00edstica)<\/li>\n\n\n\n<li>Redes neurais para embeddings ou scoring<\/li>\n\n\n\n<li>LLMs de chamada \u00fanica para <a href=\"https:\/\/cheesecakelabs.com\/blog\/classificacao-com-ia-apps-eficientes\/\" target=\"_blank\" rel=\"noreferrer noopener\">classifica\u00e7\u00e3o<\/a>\/Q&amp;A<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Sa\u00edda: previs\u00e3o, recomenda\u00e7\u00e3o, r\u00f3tulo de classe, score<\/li>\n\n\n\n<li>Deploy: empacotado como APIs ou microsservi\u00e7os<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Casos de uso<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Motores de recomenda\u00e7\u00e3o com filtragem colaborativa ou embeddings<\/li>\n\n\n\n<li>Previs\u00e3o de churn ou detec\u00e7\u00e3o de fraude<\/li>\n\n\n\n<li>Classificadores de sentimento ou pipelines de NER<\/li>\n\n\n\n<li>Consultas one-shot a LLM, como tradu\u00e7\u00e3o ou sumariza\u00e7\u00e3o<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Caracter\u00edsticas de infraestrutura<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Deploy como endpoints REST ou pipelines em batch<\/li>\n\n\n\n<li>Ciclo de vida do modelo: treinar \u2192 validar \u2192 deploy \u2192 monitorar<\/li>\n\n\n\n<li>N\u00e3o lida com percep\u00e7\u00e3o, decis\u00e3o ou orquestra\u00e7\u00e3o de a\u00e7\u00f5es<\/li>\n\n\n\n<li>Costuma ficar embarcado em aplica\u00e7\u00f5es maiores que n\u00e3o s\u00e3o de IA<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1800\" height=\"764\" src=\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-system-implementation-1.png\" alt=\"Implementacao-de-AI-Systems\n\" class=\"wp-image-12983\" srcset=\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-system-implementation-1.png 1800w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-system-implementation-1-600x255.png 600w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-system-implementation-1-1200x509.png 1200w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-system-implementation-1-768x326.png 768w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-system-implementation-1-1536x652.png 1536w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-system-implementation-1-760x323.png 760w\" sizes=\"(max-width: 1800px) 100vw, 1800px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Implementa\u00e7\u00e3o de AI Agents<\/strong><\/h2>\n\n\n\n<p>Agents introduzem uma <strong>arquitetura de controle em loop<\/strong>, na qual o racioc\u00ednio se intercala com percep\u00e7\u00e3o e a\u00e7\u00e3o.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Arquitetura modular de agent<\/strong><\/h3>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>1. Intera\u00e7\u00e3o com o ambiente<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Puxa dados em tempo real via APIs, input do usu\u00e1rio, sistemas de arquivos ou sensores<\/li>\n\n\n\n<li>Permite que os agents &#8220;sintam&#8221; o contexto em que operam<\/li>\n\n\n\n<li>O <strong>MCP<\/strong> (Model Context Protocol) simplifica e padroniza essa conex\u00e3o<br><\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>2. Percep\u00e7\u00e3o multimodal<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Converte sinais brutos (texto, voz, imagens) em embeddings ou informa\u00e7\u00f5es estruturadas<\/li>\n\n\n\n<li>Pode incluir OCR, speech-to-text, CLIP\/BLIP para input visual<br><\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>3. Motor de decis\u00e3o<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>M\u00f3dulo central de racioc\u00ednio: normalmente um LLM<\/li>\n\n\n\n<li>Refor\u00e7ado por:<br>\n<ul class=\"wp-block-list\">\n<li><strong>Retrieval-Augmented Generation (RAG)<\/strong> a partir de um banco vetorial ou de um grafo<\/li>\n\n\n\n<li><strong>Regras de neg\u00f3cio<\/strong> ou guardrails (ex.: Constitutional AI)<\/li>\n\n\n\n<li><strong>Planejamento<\/strong> para gerar estrat\u00e9gias de m\u00faltiplos passos<br><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>4. Execu\u00e7\u00e3o de a\u00e7\u00f5es<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Chama APIs externas, envia e-mails, dispara ferramentas de automa\u00e7\u00e3o<\/li>\n\n\n\n<li>O <strong>MCP<\/strong> (Model Context Protocol) simplifica e padroniza essa conex\u00e3o<\/li>\n\n\n\n<li>Costuma ser abstra\u00eddo como &#8220;tools&#8221; ou &#8220;functions&#8221; escolhidas dinamicamente<\/li>\n\n\n\n<li>Pode incluir integra\u00e7\u00f5es com calend\u00e1rios, edi\u00e7\u00e3o de documentos ou interfaces rob\u00f3ticas<br><\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>5. Mem\u00f3ria e aprendizado<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Longo prazo: guarda contexto sem\u00e2ntico (ex.: hist\u00f3rico de conversa, embeddings)<\/li>\n\n\n\n<li>Curto prazo: mem\u00f3ria de trabalho para a tarefa atual<\/li>\n\n\n\n<li>Usa <strong>bancos de dados vetoriais<\/strong> para busca por similaridade e recupera\u00e7\u00e3o de mem\u00f3ria<\/li>\n\n\n\n<li>Loops de feedback permitem ajuste cont\u00ednuo e automelhoria<br><\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>6. Camada de integra\u00e7\u00e3o<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>O <strong>MCP<\/strong> abstrai e gerencia o acesso a ferramentas e fontes de dados<\/li>\n\n\n\n<li>Facilita a integra\u00e7\u00e3o plug-and-play sem escrever wrappers customizados<\/li>\n\n\n\n<li>Torna os agents <strong>agn\u00f3sticos a ferramenta e compon\u00edveis<\/strong><strong><br><\/strong><\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1080\" height=\"1203\" src=\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agent-implementation-2.jpg\" alt=\"Implementacao-de-AI-Agents\" class=\"wp-image-12985\" srcset=\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agent-implementation-2.jpg 1080w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agent-implementation-2-539x600.jpg 539w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agent-implementation-2-1077x1200.jpg 1077w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agent-implementation-2-768x855.jpg 768w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agent-implementation-2-760x847.jpg 760w\" sizes=\"(max-width: 1080px) 100vw, 1080px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Padr\u00f5es de workflow para agents<\/strong><\/h3>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>1. Prompt chaining<\/strong><\/h4>\n\n\n\n<p>Decomp\u00f5e uma tarefa em chamadas sequenciais ao LLM. A sa\u00edda de cada passo alimenta o pr\u00f3ximo. \u00datil para racioc\u00ednio passo a passo, checagens program\u00e1ticas ou etapas de valida\u00e7\u00e3o.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1920\" height=\"800\" src=\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-prompt-chaning-3.png\" alt=\"ai-agents-prompt-chaning\" class=\"wp-image-12987\" srcset=\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-prompt-chaning-3.png 1920w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-prompt-chaning-3-600x250.png 600w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-prompt-chaning-3-1200x500.png 1200w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-prompt-chaning-3-768x320.png 768w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-prompt-chaning-3-1536x640.png 1536w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-prompt-chaning-3-760x317.png 760w\" sizes=\"(max-width: 1920px) 100vw, 1920px\" \/><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>2. Roteamento<\/strong><\/h4>\n\n\n\n<p>Classifica o input do usu\u00e1rio e o encaminha para agents, prompts ou ferramentas especializadas. Comum em agents com v\u00e1rias skills (ex.: agendamento, pesquisa, suporte).<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1920\" height=\"800\" src=\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-routing-4.png\" alt=\"ai-agents-roteamento\" class=\"wp-image-12989\" srcset=\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-routing-4.png 1920w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-routing-4-600x250.png 600w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-routing-4-1200x500.png 1200w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-routing-4-768x320.png 768w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-routing-4-1536x640.png 1536w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-routing-4-760x317.png 760w\" sizes=\"(max-width: 1920px) 100vw, 1920px\" \/><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>3. Paraleliza\u00e7\u00e3o<\/strong><\/h4>\n\n\n\n<p>Executa tarefas em paralelo:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Seccionamento<\/strong>: quebra uma tarefa em partes (ex.: resumir cap\u00edtulos de forma independente)<\/li>\n\n\n\n<li><strong>Vota\u00e7\u00e3o<\/strong>: roda v\u00e1rias gera\u00e7\u00f5es e escolhe por pontua\u00e7\u00e3o ou por maioria<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1920\" height=\"800\" src=\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-parallelization-5.png\" alt=\"ai-agents-paralelizacao\" class=\"wp-image-12991\" srcset=\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-parallelization-5.png 1920w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-parallelization-5-600x250.png 600w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-parallelization-5-1200x500.png 1200w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-parallelization-5-768x320.png 768w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-parallelization-5-1536x640.png 1536w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-parallelization-5-760x317.png 760w\" sizes=\"(max-width: 1920px) 100vw, 1920px\" \/><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>4. Padr\u00e3o orquestrador-worker<\/strong><\/h4>\n\n\n\n<p>Um agent central planeja e delega subtarefas para sub-agents. \u00datil em tarefas complexas como gera\u00e7\u00e3o de relat\u00f3rios, planejamento ou coordena\u00e7\u00e3o multimodal.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1920\" height=\"800\" src=\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-orchestrator-6.png\" alt=\"ai-agents-orquestrador\" class=\"wp-image-12993\" srcset=\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-orchestrator-6.png 1920w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-orchestrator-6-600x250.png 600w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-orchestrator-6-1200x500.png 1200w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-orchestrator-6-768x320.png 768w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-orchestrator-6-1536x640.png 1536w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-orchestrator-6-760x317.png 760w\" sizes=\"(max-width: 1920px) 100vw, 1920px\" \/><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>5. Loop avaliador-otimizador<\/strong><\/h4>\n\n\n\n<p>Junta um agent gerador com um agent revisor. A sa\u00edda \u00e9 melhorada de forma iterativa com feedback. Comum em workflows de pesquisa, idea\u00e7\u00e3o ou copy de produto.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"1920\" height=\"800\" src=\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-evaluator-optimizer-7.png\" alt=\"ai-agents-avaliador-otimizador\" class=\"wp-image-12995\" srcset=\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-evaluator-optimizer-7.png 1920w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-evaluator-optimizer-7-600x250.png 600w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-evaluator-optimizer-7-1200x500.png 1200w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-evaluator-optimizer-7-768x320.png 768w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-evaluator-optimizer-7-1536x640.png 1536w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/08\/ai-agents-evaluator-optimizer-7-760x317.png 760w\" sizes=\"(max-width: 1920px) 100vw, 1920px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Desafios de implementa\u00e7\u00e3o de agents e como resolv\u00ea-los<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Gest\u00e3o de estado<\/strong><\/h3>\n\n\n\n<p><strong>Desafio<\/strong>: como persistir e recuperar o contexto relevante de forma eficiente<br><strong>Solu\u00e7\u00e3o<\/strong>: bancos vetoriais (ex.: Pinecone, Weaviate) com filtro por metadados; gerenciadores de sess\u00e3o ou caches de curto prazo<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Integra\u00e7\u00e3o com ferramentas<\/strong><\/h3>\n\n\n\n<p><strong>Desafio<\/strong>: integrar com dezenas de APIs \u00e9 fr\u00e1gil e caro<br><strong>Solu\u00e7\u00e3o<\/strong>: o <strong>MCP<\/strong> abstrai ferramentas em &#8220;servers&#8221; interoper\u00e1veis e permite escalar r\u00e1pido sem c\u00f3digo de cola<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>3. Tratamento de erros e autocorre\u00e7\u00e3o<\/strong><\/h3>\n\n\n\n<p><strong>Desafio<\/strong>: agents podem alucinar, falhar ou entrar em loop infinito<br><strong>Solu\u00e7\u00e3o<\/strong>:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Guardrails e checagens em cada etapa<\/li>\n\n\n\n<li>Redund\u00e2ncia e vota\u00e7\u00e3o por maioria<\/li>\n\n\n\n<li>Loop de feedback com um agent avaliador<\/li>\n\n\n\n<li>Frameworks de monitoramento e rastreabilidade<br><\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>4. Otimiza\u00e7\u00e3o de custo e lat\u00eancia<\/strong><\/h3>\n\n\n\n<p><strong>Desafio<\/strong>: workflows de m\u00faltiplos passos consomem muito recurso<br><strong>Solu\u00e7\u00e3o<\/strong>:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Agents h\u00edbridos (usar modelos menores para subtarefas)<\/li>\n\n\n\n<li>Cachear resultados intermedi\u00e1rios<\/li>\n\n\n\n<li>Adiar ou agrupar em batch as a\u00e7\u00f5es n\u00e3o cr\u00edticas<\/li>\n\n\n\n<li>Fine-tune em dom\u00ednios estreitos para reduzir o uso de tokens<br><\/li>\n<\/ul>\n\n\n\n<p>AI Agents n\u00e3o s\u00e3o simplesmente AI Systems &#8220;melhores&#8221;: s\u00e3o outra esp\u00e9cie. Eles trazem autonomia, adaptabilidade e mem\u00f3ria para sistemas inteligentes. Em troca, exigem <strong>planejamento de arquitetura cuidadoso<\/strong>, <strong>workflows modulares<\/strong> e <strong>infraestrutura robusta<\/strong>.<\/p>\n\n\n\n<p>\u00c0 medida que Model Context Protocols, bancos de dados vetoriais e padr\u00f5es de orquestra\u00e7\u00e3o multi-agent amadurecem, o <a href=\"https:\/\/cheesecakelabs.com\/services\/ai-development\" target=\"_blank\" rel=\"noreferrer noopener\">desenvolvimento de IA<\/a> vai se parecer cada vez mais com o desenho de organiza\u00e7\u00f5es inteligentes. Nelas, o software n\u00e3o apenas <em>serve<\/em>, mas <em>decide<\/em>, <em>age<\/em> e <em>evolui<\/em>.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><a href=\"https:\/\/cheesecakelabs.com\/services\/ai-development\"><img decoding=\"async\" width=\"1157\" height=\"506\" src=\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/03\/CTA.png\" alt=\"Banner-pagina-de-IA\" class=\"wp-image-12612\" srcset=\"https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/03\/CTA.png 1157w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/03\/CTA-600x262.png 600w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/03\/CTA-768x336.png 768w, https:\/\/ckl-website-static.s3.amazonaws.com\/wp-content\/uploads\/2025\/03\/CTA-760x332.png 760w\" sizes=\"(max-width: 1157px) 100vw, 1157px\" \/><\/a><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Refer\u00eancias<\/strong><\/h2>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2503.12687\" target=\"_blank\" rel=\"noreferrer noopener\">AI Agents: Evolution, Architecture, and Real-World Applications<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.anthropic.com\/engineering\/building-effective-agents\" target=\"_blank\" rel=\"noreferrer noopener\">Building effective agents<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/arxiv.org\/pdf\/2503.23278\" target=\"_blank\" rel=\"noreferrer noopener\">Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/modelcontextprotocol.io\/\" target=\"_blank\" rel=\"noreferrer noopener\">Model Context Protocol<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/medium.com\/@sagarmadhukar.jadhav\/ai-agents-vs-other-ai-systems-definitions-and-distinctions-1ec35a67e714\" target=\"_blank\" rel=\"noreferrer noopener\">AI Agents vs. Other AI Systems: Definitions and Distinctions<\/a><\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>As aplica\u00e7\u00f5es de IA passam por uma transforma\u00e7\u00e3o estrutural. O padr\u00e3o antigo eram AI Systems est\u00e1ticos e movidos a pipeline, como motores de recomenda\u00e7\u00e3o ou classificadores. O que se v\u00ea agora \u00e9 a mudan\u00e7a para AI Agents: entidades din\u00e2micas e aut\u00f4nomas, capazes de perceber, raciocinar e agir com base no contexto do mundo real. Essa [&hellip;]<\/p>\n","protected":false},"author":92,"featured_media":12981,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_yoast_wpseo_title":"AI Agents vs AI Systems: o que muda na arquitetura","_yoast_wpseo_metadesc":"AI Agents vs AI Systems: as diferen\u00e7as de arquitetura de software, como implementar cada um e por que o padr\u00e3o antigo j\u00e1 n\u00e3o d\u00e1 conta.","_yoast_wpseo_meta-robots-noindex":"","_yoast_wpseo_canonical":"","ai_summary":"As aplica\u00e7\u00f5es de IA est\u00e3o migrando de AI Systems est\u00e1ticos e movidos a pipeline (como motores de recomenda\u00e7\u00e3o e classificadores) para AI Agents: entidades din\u00e2micas e aut\u00f4nomas que percebem, raciocinam e agem com base no contexto.\nAI Systems s\u00e3o sem estado, com fluxo de controle linear, sem mem\u00f3ria e guiados por tarefa; AI Agents s\u00e3o com estado, guiados por objetivo, com mem\u00f3ria persistente (ex.: bancos vetoriais), uso adaptativo de ferramentas via APIs e aprendizado ativo a partir do ambiente.\nA arquitetura modular de um agent inclui intera\u00e7\u00e3o com o ambiente, percep\u00e7\u00e3o multimodal, motor de decis\u00e3o (normalmente um LLM refor\u00e7ado por RAG e guardrails), execu\u00e7\u00e3o de a\u00e7\u00f5es, mem\u00f3ria e uma camada de integra\u00e7\u00e3o baseada no MCP (Model Context Protocol); padr\u00f5es de workflow incluem prompt chaining, roteamento, paraleliza\u00e7\u00e3o, orquestrador-worker e loop avaliador-otimizador.\nOs principais desafios de implementa\u00e7\u00e3o s\u00e3o gest\u00e3o de estado, integra\u00e7\u00e3o com ferramentas, tratamento de erros\/autocorre\u00e7\u00e3o e otimiza\u00e7\u00e3o de custo e lat\u00eancia, resolvidos com bancos vetoriais, MCP, guardrails, vota\u00e7\u00e3o por maioria, agents avaliadores, modelos menores para subtarefas e cache de resultados intermedi\u00e1rios.","ai_summary_en":"","ai_summary_pt-br":"As aplica\u00e7\u00f5es de IA est\u00e3o migrando de AI Systems est\u00e1ticos e movidos a pipeline (como motores de recomenda\u00e7\u00e3o e classificadores) para AI Agents: entidades din\u00e2micas e aut\u00f4nomas que percebem, raciocinam e agem com base no contexto.\nAI Systems s\u00e3o sem estado, com fluxo de controle linear, sem mem\u00f3ria e guiados por tarefa; AI Agents s\u00e3o com estado, guiados por objetivo, com mem\u00f3ria persistente (ex.: bancos vetoriais), uso adaptativo de ferramentas via APIs e aprendizado ativo a partir do ambiente.\nA arquitetura modular de um agent inclui intera\u00e7\u00e3o com o ambiente, percep\u00e7\u00e3o multimodal, motor de decis\u00e3o (normalmente um LLM refor\u00e7ado por RAG e guardrails), execu\u00e7\u00e3o de a\u00e7\u00f5es, mem\u00f3ria e uma camada de integra\u00e7\u00e3o baseada no MCP (Model Context Protocol); padr\u00f5es de workflow incluem prompt chaining, roteamento, paraleliza\u00e7\u00e3o, orquestrador-worker e loop avaliador-otimizador.\nOs principais desafios de implementa\u00e7\u00e3o s\u00e3o gest\u00e3o de estado, integra\u00e7\u00e3o com ferramentas, tratamento de erros\/autocorre\u00e7\u00e3o e otimiza\u00e7\u00e3o de custo e lat\u00eancia, resolvidos com bancos vetoriais, MCP, guardrails, vota\u00e7\u00e3o por maioria, agents avaliadores, modelos menores para subtarefas e cache de resultados intermedi\u00e1rios.","footnotes":"","ckl_wpml_lang":"br","ckl_wpml_source_id":12978,"ckl_wpml_status":"ok: lang=br trid=54697 source=12978 at 2026-08-14 16:47:38"},"categories":[432,471],"tags":[1327,1326,54,1150,1199],"class_list":["post-14542","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-engineering","category-engenharia","tag-ai-agents","tag-artificial-inteligence","tag-tag-mobile-app-development","tag-tag-mobile-app-development-br","tag-software-development"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - 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