I built something similar to this before Langraph had their agent builder @braid.ink, because Claude Code kept referencing old documentation. But the problem ended up solving itself when Langraph came out with their agent builder, and Claude Code can better navigate its documentation.

The only thing I would mention is that building a lot of agents and working with a lot of plug-ins and MCPs is everything is super situation- and context-dependent. It's hard to spin up a general agent that's useful in a production workflow because it requires so much configuration from a standard template. And if you're not being very careful in monitoring it, then it won't meet your requirements when it's completed, when it comes to agents, precision and control is key.

This really resonates - the opacity problem is exactly what makes MCP-based agents hard to trust in production. You can't control what you can't see.

We built toran.sh specifically for this: it lets you watch real API requests from your agents as they happen, without adding SDKs or logging code. Replace the base URL, and you see exactly what the agent sent and what came back.

The "precision and control" point is key though - visibility is step one, but you also need guardrails. We're working on that layer too (keypost.ai for policy enforcement on MCP pipelines).

Would love to hear what monitoring approaches you've found work well for production agent workflows.