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Coding agents are getting better at writing code. But when it comes to debugging real-world bugs, most AI still struggles — not because the models are weak, but because they lack runtime context.
Why coding agents fail after multiple debugging attempts is not a prompt issue or a model flaw, but a context problem. Repeated retries distort attention, weaken invariants, and trap agents in failing loops, making better context more valuable than more attempts.
We repurposed OpenTelemetry (OTel) from a production observability tool into a local, zero-config debugging context specifically for AI coding agents like Cursor and Copilot.
Cursor recently released Debug Mode, attempting to solve the AI debugging problem. In this blog, we'll talk about its mechanism, pros and cons, and future prospects.
A real-world Java debugging case showing why AI debugging without runtime context leads to token waste and trial-and-error, and how Syncause changes that.
We're thrilled to kick off the beta for Syncause AI debugger, our new tool designed to make debugging with AI actually work.
