AI RAG代理框架的MCP替代方案
如果您尝试在模型上下文协议（MCP）基础上构建AI代理系统，您可能遇到了与我们相同的问题：集成复杂性、缺乏用户界面支持、高昂的令牌成本以及容易产生幻觉的输出。这就是我们构建更好解决方案的原因——Oqlous AI的RAG代理框架，从零开始设计，旨在实用、可扩展且用户友好。
让我为您详细说明。
MCP的问题在哪里？
虽然MCP提出了关于管理AI上下文和行动工作流的有趣想法，但它存在一些关键的局限性：
没有用户界面/最终用户层
MCP没有提供原生的用户界面支持。您提示它创建一个JIRA工单，得到的只是文本回复。就这样，没有交互层，没有原生应用的用户界面。
令牌效率低下
MCP代理快速消耗令牌，导致成本增加和吞吐量降低。这对于实时或生产使用来说并不可扩展。
执行浅显
MCP无法进行真正的多应用、多跳推理。它不能接收一个任务，从三个应用中提取数据，综合决策，然后执行后续操作。它的深度就是这么有限。
幻觉和脆弱性
输出质量不可靠。响应可能模糊、产生幻觉或与业务上下文不一致。定制化程度极低。
Oqlous AI RAG代理框架：为真实执行而生
我们构建Oqlous AI来解决上述所有问题，甚至更多。
一键应用集成
无需手动配置文件或外部协调器。您可以通过点击连接到Gmail、JIRA、Notion、Drive等工具，拥有超过100种集成。
端到端用户界面支持
当您提示代理“创建一个JIRA任务”时，您得到的不仅仅是文本——您会在工作流中获得一个完整的JIRA用户界面组件。您可以与其互动，更新字段，拖动工单等，就像在原生应用中一样。
高效的LLM使用
得益于智能令牌管理和模块化的RAG策略，Oqlous AI每次操作消耗的令牌显著减少。这意味着执行速度快至三倍，成本更低，同时保持响应的准确性。
深度代理工作流
Oqlous AI代理能够跨多个工具进行推理。例如，您询问：“与Alice安排会议，概括最新的工程报告，并在Asana中创建后续任务。”Oqlous代理将从Notion获取报告，解析行动项，通过日历安排会议，并推送任务——这一切都是自主完成的。
可定制的企业工作流
每个企业都有独特的需求。Oqlous AI的框架允许轻松定制代理行为、集成和保护措施。您不必受限于僵化的链条或黑箱流程。
扎实、可靠的输出
通过RAG加上精细调整的执行层，幻觉现象大幅减少。代理不会猜测——它们会基于实际数据进行检查、验证和行动。
总结
MCP曾有潜力，但并不适合大规模的真实世界执行。Oqlous AI的RAG代理框架则适合。
如果您在寻找一个企业级、高效且深度互动的AI代理系统，Oqlous AI是MCP未能实现的升级版。
我们正在向开发者、初创企业和构建下一代代理应用的企业开放这一平台。欢迎与任何在这个领域工作的人联系。
很高兴为您提供访问权限：https://www.oqlous.com/get-started
查看原文
If you've tried building AI agentic systems on top of Model Context Protocol (MCP), you've likely run into the same issues we did: integration complexity, lack of UI support, high token costs, and hallucination-prone outputs. That’s why we built something better—Oqlous AI’s RAG Agentic Framework, designed from the ground up to be practical, scalable, and user-friendly.<p>Let me break it down.<p>What’s Wrong with MCP?<p>While MCP introduced an interesting idea around managing AI context and action workflows, it suffers from some critical<p>limitations:<p>No UI/End-User Layer<p>MCP provides no native UI support. You prompt it to create a JIRA ticket, and you get a text response. That’s it. No interactive layer, no native app UIs.<p>Token Inefficiency<p>MCP agents burn through tokens quickly, leading to higher cost and slower throughput. Not scalable for real-time or production use.<p>Shallow Execution<p>There’s no real multi-app, multi-hop reasoning. MCP can’t take a task, pull data from three apps, synthesize a decision, and then execute downstream actions. It just doesn't go that deep.<p>Hallucinations and Fragility<p>Output quality is unreliable. Responses can be vague, hallucinated, or misaligned with business context. Customization is minimal.<p>Oqlous AI RAG Agentic Framework: Built for Real Execution
We built Oqlous AI to solve all of the above—and more.<p>One-Click App Integrations<p>No need for manual config files or external orchestrators. You can connect to tools like Gmail, JIRA, Notion, Drive, and more with a click having 100+ integrations.<p>End-to-End UI Support<p>When you prompt the agent to "create a JIRA task," you don’t get just text—you get a full JIRA UI component within the workflow. You can interact with it, update fields, drag tickets, and more, like you would in the native app.<p>Efficient LLM Usage<p>Thanks to smart token management and modular RAG strategies, Oqlous AI consumes significantly fewer tokens per operation. That means up to three times faster execution and lower costs, while keeping responses grounded.<p>Deep Agentic Workflows<p>Oqlous AI agents can reason across multiple tools. Say you ask, "Schedule a meeting with Alice, summarize the latest engineering report, and create follow-up tasks in Asana." Oqlous agents will fetch the report from Notion, parse action items, schedule via Calendar, and push tasks—all autonomously.<p>Customizable to Enterprise Workflows<p>Every enterprise has unique needs. Oqlous AI’s framework allows easy customization of agent behavior, integrations, and guardrails. You’re not stuck with rigid chains or black-box flows.<p>Grounded, Reliable Output<p>With RAG plus fine-tuned execution layers, hallucinations are drastically reduced. Agents don’t guess—they check, verify, and act based on actual data.<p>Summary<p>MCP had promise but isn't built for real-world execution at scale. Oqlous AI’s RAG Agentic Framework is.<p>If you're looking for an enterprise-ready, highly efficient, and deeply interactive AI agent system, Oqlous AI is the upgrade MCP never became.<p>We're opening this up for developers, startups, and enterprises building the next generation of agentic applications. Happy to connect with anyone working in this space.<p>Happy to give you acess: https://www.oqlous.com/get-started