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AI & Automation
Tool CallingCustom WorkflowsRAG Pipelines

Serviços de Desenvolvimento de Agentes IA

Fluxos de Trabalho Personalizados, Chamadas de Ferramentas, Resultados Reais

< 2s
Agent Response Time
P95 latency target
40+
Agents Deployed
Across client projects
99.5%
Uptime SLA
Production environments
$0
Vendor Lock-in
You own the code
What Is AI Agent Development?

AI agent development is about building software that uses large language models to reason, plan, and execute multi-step tasks without constant hand-holding. These aren't chatbots. Agents call external tools — APIs, databases, file systems — to actually get things done. Custom workflows define the decision logic, guardrails, and approval checkpoints that keep everything from going sideways once it hits production.

Onde os projetos falham

Your chatbot answers questions but can't actually do anything Users figure this out fast, and they leave.
LLM hallucinations are a real liability in customer-facing workflows One wrong answer in finance or healthcare doesn't just embarrass you — it can cost millions.
You've prototyped with ChatGPT but can't get it production-ready The demo works great in staging while your competitors are already shipping.
Agent frameworks keep shifting — LangChain, CrewAI, AutoGen — and your engineers are burning time on framework churn instead of solving actual business problems. Engineering time burned on framework churn instead of business logic
You've got no visibility into what the agent's doing or why Black-box behavior makes debugging painful and audits nearly impossible.
Sensitive data can't leave your infrastructure for external API calls Uncontrolled LLM traffic is a compliance violation waiting to happen.

Conformidade

Structured Tool Calling

We define typed tool schemas so agents call your APIs with validated parameters — no prompt-hacking required. Every tool invocation gets logged and is fully auditable.

Multi-Step Workflow Orchestration

Complex tasks get broken into deterministic workflow graphs with conditional branching. Agents follow defined paths while still reasoning through each individual step.

RAG Pipeline Integration

Retrieval-augmented generation keeps agent responses grounded in your actual data. We build vector search with pgvector and tune chunking strategies to match your specific content structure.

Human-in-the-Loop Safeguards

Critical actions don't execute until a human approves them. Configurable escalation rules give you precise control over exactly when the agent stops and asks for permission.

Full Observability & Tracing

Every agent run is traced end-to-end — reasoning steps, tool calls, token usage, latency. Dashboards and alerting ship on day one, not as an afterthought.

Data Privacy & Self-Hosting

We'll deploy on your own infrastructure so no data leaves your VPC. Self-hosted LLMs and private API endpoints are fully supported for regulated industries.

O que construímos

Custom Tool Definitions

Typed, versioned tool schemas that connect agents to your existing APIs, databases, and internal services.

Conditional Workflow Graphs

Visual and code-defined workflow DAGs with branching, retry logic, and parallel execution.

Model-Agnostic Architecture

Swap between OpenAI, Anthropic, Mistral, or self-hosted models without rewriting your agent logic.

Streaming Response UI

Real-time streaming interfaces that show users exactly what the agent's doing as it works.

Evaluation & Testing Suite

Automated eval harnesses that test agent behavior against golden datasets before every deploy.

Cost & Token Management

Per-user and per-workflow token budgets with automatic model downgrade when limits get close.

Nosso processo

01

Agent Architecture Workshop

We map your business processes to agent capabilities. You'll walk away with a workflow diagram, tool inventory, and risk assessment for every automated action.
Week 1
02

Tool & Schema Development

We build typed tool definitions, connect your APIs, and handle authentication and error handling for every external system the agent will touch.
Week 2-3
03

Workflow & Prompt Engineering

Multi-step workflows get built and tested in full. Prompts get engineered with structured outputs, few-shot examples, and guardrails against the failure modes we see most often.
Week 3-4
04

Evaluation & Hardening

We run the agent against adversarial test suites and measure accuracy, latency, and cost. Edge cases get documented and handled — nothing goes to production until it does.
Week 5
05

Deploy & Monitor

Production deployment includes observability, alerting, and a 30-day support window. We also train your team on prompt tuning, eval maintenance, and scaling so you're not dependent on us forever.
Week 6
Next.jsVercelSupabaseOpenAIAnthropicLangChainVercel AI SDKPostgreSQLpgvector

Perguntas frequentes

Qual é a diferença entre um agente IA e um chatbot?

Um chatbot responde a mensagens. Um agente IA raciocina sobre uma tarefa, chama ferramentas externas — APIs, bancos de dados, sistemas de arquivos — e executa fluxos de trabalho multi-etapas por conta própria. Estamos falando de agendar compromissos, processar reembolsos, gerar relatórios, disparar ações reais. Não apenas responder com texto.

Quais modelos de LLM você usa para agentes IA?

Somos agnósticos em relação ao modelo. A maioria dos projetos acaba usando uma mistura — GPT-4o ou Claude para raciocínio complexo, algo mais leve como GPT-4o-mini para etapas simples de classificação. Nossa arquitetura permite trocar modelos por etapa de fluxo de trabalho, para que você esteja equilibrando custo e qualidade ao mesmo tempo. Também suportamos modelos auto-hospedados via Ollama ou vLLM se você precisar manter tudo on-prem.

Como você evita alucinações de agentes IA?

Três camadas. Chamadas de ferramentas estruturadas com esquemas tipados forçam saídas válidas. Pipelines RAG mantêm respostas ancoradas em seus dados reais. Pontos de verificação com intervenção humana capturam casos extremos antes que ações de alto risco sejam executadas. Por cima disso, suites de avaliação automatizadas sinalizam regressões de precisão antes de cada implantação.

Agentes IA podem se integrar com nosso software existente?

Sim. Agentes podem se conectar a qualquer coisa com uma API — CRMs, ERPs, bancos de dados, plataformas de email, processadores de pagamento. Construímos definições de ferramentas tipadas para cada integração com autenticação adequada, rate limiting e tratamento de erros integrados. Sem API? Podemos construir uma, ou usar automação de navegador como ponte.

Quanto tempo leva para construir um agente IA customizado?

Um agente focado em um único fluxo de trabalho normalmente leva 4–6 semanas do início até a produção. Sistemas multi-agentes com várias integrações de ferramentas e fluxos de aprovação geralmente levam 8–12 semanas. Cada projeto inclui uma janela de 30 dias pós-lançamento para ajuste de prompts e otimização de desempenho.

Nossos dados são seguros ao usar agentes IA?

Quando a sensibilidade dos dados exige, implantamos inteiramente em sua infraestrutura. Para indústrias reguladas, agentes podem rodar dentro de seu VPC sem que nada saia da sua rede. Suportamos endpoints de LLM privados, criptografamos todos os dados em repouso e em trânsito, e fornecemos logs de auditoria completos de cada ação e chamada de ferramenta do agente.

AI Agent Development from $12,000
Fixed-fee. 30-day post-launch support.
See all packages →
Next.js DevelopmentCore Web Vitals OptimizationCore Web Vitals Complete Guide 2026

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