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AI & Automation
Tool CallingCustom WorkflowsRAG Pipelines

AI Agent 开发服务

自定义工作流、工具调用、真实成果

< 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.

项目失败的原因

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.

合规

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.

我们构建的内容

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.

我们的流程

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

常见问题

AI Agent 和聊天机器人有什么区别?

聊天机器人回应消息。AI Agent 对任务进行推理,调用外部工具——API、数据库、文件系统——并自主执行多步骤工作流。我们说的是预约、处理退款、生成报告、触发真实操作。不仅仅是回复文本。

你们为 AI Agent 使用哪些 LLM 模型?

我们是模型无关的。大多数项目最终会使用混合模式——用 GPT-4o 或 Claude 进行复杂推理,用 GPT-4o-mini 这样的轻量级模型进行简单分类步骤。我们的架构允许你为每个工作流步骤交换模型,以同时平衡成本和质量。如果需要将所有内容保持在本地,我们还通过 Ollama 或 vLLM 支持自托管模型。

你们如何防止 AI Agent 幻觉?

三个层级。结构化工具调用使用类型化模式强制有效输出。RAG 管道使响应基于你的实际数据。人工干预检查点在高风险操作执行前捕捉边界情况。此外,自动化评估套件在每次部署前标记准确性回归。

AI Agent 能否与我们现有软件集成?

可以。Agent 可以连接到任何具有 API 的东西——CRM、ERP、数据库、电子邮件平台、支付处理器。我们为每个集成构建类型化的工具定义,内置适当的身份验证、速率限制和错误处理。没有 API?我们可以构建一个,或使用浏览器自动化作为桥接。

构建自定义 AI Agent 需要多长时间?

一个专注的单工作流 Agent 通常需要 4-6 周从启动到生产。具有多个工具集成和审批工作流的多 Agent 系统通常需要 8-12 周。每个项目都包括 30 天的上线后窗口,用于提示调整和性能优化。

使用 AI Agent 时我们的数据安全吗?

当数据敏感性要求时,我们完全在你的基础设施上部署。对于受监管的行业,Agent 可以在你的 VPC 内运行,任何数据都不离开你的网络。我们支持私有 LLM 端点,对所有静态和传输中的数据进行加密,并为你提供每个 Agent 操作和工具调用的完整审计日志。

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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We'll deliver a quote within 24 hours.

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