Stop becoming the infrastructure team for your AI agents

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?