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.

Let me break it down.

What’s Wrong with MCP?

While MCP introduced an interesting idea around managing AI context and action workflows, it suffers from some critical.

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.

MCP agents burn through tokens quickly, leading to higher cost and slower throughput. Not scalable for real-time or production use.

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.

Output quality is unreliable. Responses can be vague, hallucinated, or misaligned with business context. Customization is minimal.

Oqlous AI RAG Agentic Framework: Built for Real Execution We built Oqlous AI to solve all of the above - and more.

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.

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.

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.

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.

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.

With RAG plus fine-tuned execution layers, hallucinations are drastically reduced. Agents don’t guess — they check, verify, and act based on actual data.

Summary

MCP had promise but isn’t built for real-world execution at scale. Oqlous AI’s RAG Agentic Framework is.

If you’re looking for an enterprise-ready, highly efficient, and deeply interactive AI agent system, Oqlous AI is the upgrade MCP never became.

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.