I Spent $40 in API Credits Before I Realized AI Was Solving the Wrong Problem.

I Spent $40 in API Credits Before I Realized AI Was Solving the Wrong Problem. Built AgentProbe to empower LLM with debugging power and more

AgentProbe doesn’t auto-fix your bugs. It does something harder — it stops you from paying to generate the wrong fix three times in a row.

The thing nobody wants to admit out loud

You paste a stack trace. The model returns something confident. You apply it. The bug is still there — or worse, now there are two bugs.

So you paste again, with more context. The model returns something else. Also confident.

Repeat until you’ve burned 45 minutes, $15 in tokens, and your trust in the tool.

Here’s what I’ve come to believe: that loop isn’t an AI problem. It’s a process problem. The model is doing exactly what it was designed to do — produce output. But debugging isn’t a production task. It’s an inference task. You don’t need an answer. You need the right question.

Most AI assistants skip the question entirely. That’s the bug in the system, not in your code.

What AgentProbe actually does (no marketing fluff)

AgentProbe is an intelligence layer that sits between your question and the model’s answer. It does three things:

1. Classifies the error locally . A built-in taxonomy of error shapes (null reference, type mismatch, uninitialized state, async timing, missing resource, and more) catches the common 80% instantly.

2. Asks a Socratic question instead of emitting a patch. When it knows the shape of the failure, it asks you something targeted — the kind of question that makes you go “oh.” That’s not a limitation. That’s the whole point.

3. Lets the LLM use your actual debugger. This is the part that surprised me most. When you’ve started a process with — inspect, debugpy, or JDWP, AgentProbe can hand the model a live attach — it sets a real breakpoint, reads the local variable values at that line, and reports back what is actually true at runtime. Not a guess. Not a hallucination from the stack trace. The real values.

It gives the model a debugger instead of a crystal ball.

The 3 things that make it different:

1. Evidence before action — validate_hypothesis returns a hard true/false with file + line evidence before a single character is changed.
2. Pre-flight safety check — check_suggestion scans every proposed fix against logic guards and known violation patterns before the edit lands.
3. Audit trail that doesn’t disappear — Every debug session saved as structured JSON to

.agentprobe/sessions/

— root cause, fix, evidence, files involved. Searchable by any teammate months later. On-call investigations go from 5 minutes to 30 seconds.

Try it: https://agentprobe.space

No guessing. No hallucinated variable names. No “fix then pray” deployments.

I’d love to hear what task you tried on AgentProbe and how it performed for you.