3 posts tagged with "Debugging"

Your agent fails midway through a task. The trace is right there in vLLora, but it's 200 spans deep. You start scrolling, scanning for the red error or the suspicious tool call. Somewhere in those spans is the answer, but finding it takes longer than it should.

Today we're launching Lucy, an AI assistant built directly into vLLora that reads your traces and tells you what went wrong. You ask a question in plain English, Lucy inspects the trace, and you get a diagnosis with concrete next steps. Lucy is available now in beta.

Your agent returns the right answer. The status is 200 OK, and the user walks away satisfied. On the surface, everything looks fine. But when you check the API bill, it doesn’t line up with how simple the task actually was.

LLMs are unusually resilient. When a tool call fails, they don’t stop execution. They try again with small variations. When a response looks off, they adjust and keep going. That behavior is often helpful, but it can also hide broken execution paths. The user sees a successful result, while your token usage quietly absorbs retries, fallbacks, and extra reasoning that never needed to happen.

If you’re building agents with tools like Claude Code or Cursor, or you prefer working in the terminal, you’ve probably hit this friction already. Your agent runs, something breaks partway through, and now you have to context-switch to a web UI to understand what happened. You search for the right trace, click through LLM calls, and then try to carry that context back into your editor.

vLLora’s MCP Server removes that context switch. Your coding agent becomes the interface for inspecting traces, understanding failures, and debugging agent behavior — without leaving your editor or terminal.