Introducing AgentProbe: Structured Debugging for AI Coding Agents

Most AI Coding Assistants Guess at Bugs. Most AI coding assistants are good at writing code. But when something breaks, they still tend to debug the same way a tired engineer does at 2:13 a.m.:search, patch, and hope.

That’s the gap I built AgentProbe to close.AgentProbe gives GitHub Copilot 25 MCP tools to debug with evidence — not hunches.

Instead of jumping straight into edits, it helps an AI coding assistant:trace actual value flow, validate root causes, check risky fixes before they land , can attach to a running debugger, allowing Copilot to step through code at user-defined breakpoints. and leave behind a searchable audit trail.

Here’s the difference

Root cause

Copilot Alone: Grep + guess With AgentProbe: Instant pattern match

Validation

Copilot Alone: Run it and see With AgentProbe: Verified before the first edit

Guard coverage

Copilot Alone: Hope you remembered With AgentProbe: Surfaced automatically

Hallucinations

Copilot Alone: Invented variable names With AgentProbe: Statically confirmed

Deploy confidence

Copilot Alone: Fix then pray With AgentProbe: Audit then ship

Documentation

Copilot Alone: Written from memory With AgentProbe: Auto-generated audit trail

What makes AgentProbe different

A lot of debugging tools help you inspect.

AgentProbe is built to help AI reason with proof.

1) Evidence before action

Before a single character is changed, validate_hypothesis returns a hard true / false based on actual file and line evidence.That means the agent isn’t “trying a fix to see what happens.”It’s first answering a more important question:“Do we know this is the bug?”

2) Pre-flight safety checks

Before any suggested fix lands, check_suggestion scans it against:

• logic guards,
• known violation patterns,
• and failure-prone edit behavior.

So instead of shipping a plausible-looking patch, the agent gets a risk screen before execution.That matters more than people think.A lot of “AI debugging” is really just AI patch generation with weak verification.Those are not the same thing.

3) An audit trail that doesn’t disappear

Every debug session is saved as structured JSON inside:

.agentprobe/sessions/        

That includes:

• root cause
• evidence
• files involved
• and the final fix path.

So six months later, a teammate can answer:“Why did we change this?” without relying on Slack archaeology or someone’s memory.That changes on-call work in a very practical way:

Investigations that used to take 5 minutes can drop to 30 seconds.

The numbers

• 25 MCP tools across 5 categories
• 30+ error patterns with 80% rule match rate
• 0 extra API cost — the rule engine runs locally
• Supports JS/TS, Python, and Java

Why this matters

The biggest risk with AI-assisted development isn’t that the code is generated.It’s that teams start trusting changes that were never properly verified.That’s where bad fixes slip through:

• wrong assumptions,
• invented variables,
• missing guard logic,
• and “looks right” edits that quietly create new failures.

AgentProbe is designed to make AI debugging feel less like autocomplete with confidence and more like engineering with evidence.

**No guessing.

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

Try it: agentprobe.space

#AI #Debugging #DeveloperTools #GitHubCopilot #SoftwareEngineering