Vectoralix

I started noticing the same pattern again and again.

Building a quick AI agent demo is not that hard anymore. You connect a model, add a few tools, maybe plug in an MCP server, and it feels like magic for the first few days.

Then the boring problems start.

Where do we keep the project knowledge?
How do we update it?
Who is allowed to use which tool?
How do we test changes before the agent starts using them?
Why did the agent call the wrong tool?
Why are we copy-pasting the same context into every new chat?

None of this is the “cool AI” part.

But without it, the agent becomes fragile very quickly.

That is the problem I am working on with Vectoralix.

The idea is to give teams one place where they can manage the knowledge, tools, and context their agents need. Instead of every developer setting up their own mess of docs, prompts, local MCP servers, and copied project context, the team can share one controlled layer.

I do not think most small teams want to build an internal agent platform.

They just want their agents to understand the project, use the right tools, and behave consistently.

That is where I think the market is going. The hard part will not be “can we connect an LLM to a tool?” The hard part will be keeping the whole thing usable when more people, more tools, and more project knowledge get involved.

Curious if others here are seeing the same thing.

If you use AI agents in your product or workflow, what became annoying first?

Context, tool management, authentication, testing, or just maintaining the whole setup?

Hi everyone! I'm Eugene, a software architect and full-stack developer. I've spent the last few months deep in the weeds of AI voice agents and agentic workflows, and I kept running into the same frustrating infrastructure bottleneck.

The Model Context Protocol (MCP) is rapidly becoming the standard for connecting AI models to data sources, but the actual deployment and hosting of these servers—specifically handling .mcpb manifests—is still unnecessarily complex.

There wasn't a dedicated, streamlined way to manage and host these at scale. So, I decided to build one.

Enter Vectoralix

I recently launched Vectoralix—a registry and managed hosting platform specifically designed for MCP servers. My goal is to take the infrastructure headache away from developers building AI agents, allowing them to focus entirely on their core logic rather than DevOps.

Under the Hood

As a backend developer, I wanted the architecture to be incredibly performant from day one. I'm building the core platform with PHP 8.4 and Laravel, utilizing strict typing and modern architectural patterns. To squeeze out every drop of performance, the entire stack runs on FrankenPHP and Caddy, which takes Laravel's speed to a completely different level.

For the heavy lifting on the infrastructure side, I'm orchestrating everything with Docker, Terraform, and AWS to ensure the managed hosting can scale instantly on demand. I also rely heavily on "agent-first" development workflows and automated code quality tools like PHPStan, deptrac to keep the codebase pristine while moving fast as a solo founder.

What's Next

Right now, my main focus is refining the documentation and getting early user feedback.

If you are building AI agents or working with the Model Context Protocol, I would love to hear how you are currently handling your server deployments. Would a managed registry solve your pain points?

Any feedback on the platform's core concept or the landing page would be hugely appreciated!

https://vectoralix.com