Debugging with Hermes AI: A Systematic Approach to Troubleshooting Code

You know the feeling. Your tests fail. The stack trace is 40 lines deep. The error message says something about a NoneType that shouldn't exist. You've been staring at the same function for 20 minutes and it looks fine.

This is where most developers spin their wheels — adding print statements, commenting out code, changing things at random hoping something sticks.

Hermes AI takes a different approach. It doesn't just dump code at you. It follows a structured debugging methodology that mirrors how senior engineers actually solve problems: reproduce, isolate, hypothesize, verify, fix.

Here's how to use it effectively.

The Debugging Skill: Your First Line of Defense

Hermes ships with a built-in systematic-debugging skill. When you hit a bug, you don't need to describe it perfectly — just point the agent at the failure and say "debug this."

> The test suite is failing on test_user_permissions. Debug it.

The skill kicks in automatically. It follows a clear sequence:

1. Reproduce — Run the failing test or command and capture the actual output
2. Isolate — Determine the minimum scope where the bug occurs (which module, which function, which line)
3. Hypothesize — Form 2–3 likely root causes based on the evidence
4. Verify — Test each hypothesis by reading the relevant code, checking logs, or running targeted queries
5. Fix — Apply the minimal change that resolves the root cause
6. Confirm — Re-run the test suite to verify the fix didn't break anything else

That last step is the one most developers skip. And it's the one that turns a "fix" into a reliable fix.

Live Debugging with debugpy

Sometimes a failing test isn't enough context. You need to see what's happening at runtime — watch variables change, inspect object state, step through execution.

Hermes includes a python-debugpy skill that starts a live debug server you can connect to from any DAP-compatible client (VS Code, PyCharm, or even a raw terminal).

> Start a debugpy server on port 5678 and set a breakpoint in auth/permissions.py line 42

The agent will:

1. Inject debugpy into your process
2. Set breakpoints at the locations you specify
3. Wait for you to attach a debugger client
4. Optionally inspect variables and report back

This is particularly useful for:

• Race conditions that only show up under load
• State corruption that happens deep in a request pipeline
• Third-party library behavior you need to observe, not just read

Subagent Triage: Parallel Debugging

Here's where Hermes gets genuinely powerful. When you're facing a complex bug with multiple possible causes, you can delegate parallel investigation to subagents.

> The payment processing pipeline is failing intermittently. 
> Delegate one subagent to check the database connection pool, 
> another to review the Stripe API error logs, and a third to 
> inspect the queue worker configuration.

Each subagent gets its own terminal session and context window. They investigate in parallel — no sequential context pollution. When they finish, they report back their findings, and you (or the orchestrating agent) synthesize the results into a single diagnosis.

This pattern — which the subagent delegation skill calls "research and triage" — turns a 2-hour debugging session into a 15-minute parallel investigation.

Pattern: The Bug Report Workflow

For teams, I recommend standardizing on a bug report template that works with Hermes's debugging skill. Here's what I use:

## Bug: [Short title]

**Symptom**: What's the observable failure?
**Repro steps**: Exact commands or actions to trigger it
**Expected**: What should happen
**Actual**: What happens instead
**Environment**: OS, Python/Node version, dependency versions
**Recent changes**: What changed last (deployment, config, dependency bump)

When you feed this into Hermes:

> Here's a bug report. Debug it systematically:
> [paste the report]

The agent has everything it needs — reproduction steps, environment context, and a timeline of recent changes. No back-and-forth clarification needed.

Pattern: Regression Hunting

When a previously working feature breaks, the fastest path is usually git bisect. Hermes can automate this:

> The user export feature broke sometime in the last 50 commits. 
> The test at tests/test_export.py passes on v2.3.0 but fails on main. 
> Run git bisect to find the breaking commit.

The agent will:

1. Set up the bisect range
2. Run the test at each checkpoint
3. Narrow down to the exact commit
4. Show you the diff and explain why it broke

This takes 5 minutes instead of an hour of manual binary search.

Common Pitfalls

A few things I've learned debugging with Hermes:

• Don't give it the answer. If you say "I think it's the database connection," the agent will anchor on that hypothesis. Describe the symptom, not your theory.
• Run the failing test first. The debugging skill works best when it can see the actual error output. Let it reproduce the failure before you explain what you've tried.
• Use subagents for breadth, not depth. Parallel investigation is for covering multiple possible causes, not for deep-diving into one theory. Give each subagent a narrow, distinct scope.
• Verify after every fix. The temptation is to fix and move on. The debugging skill always includes a verification step — let it run. A "fix" that breaks three other things is worse than the original bug.

When to Use What

Scenario	Approach
Single failing test	systematic-debugging skill
Intermittent failure	python-debugpy with live breakpoints
Multiple possible causes	Subagent triage (parallel investigation)
Regression in recent commits	Automated git bisect
Complex multi-service bug	Subagent triage + debugpy on suspect services

The Meta-Lesson

The real productivity gain isn't that Hermes fixes bugs faster — it's that it prevents the time waste of unstructured debugging. The skill forces a discipline: reproduce before hypothesize, verify before move on.

That discipline is the difference between a 2-hour rabbit hole and a 15-minute fix. And it's the same discipline every senior engineer uses, just systematized and enforced by the agent.

Use the skill. Trust the process. Ship the fix.

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This post is part of the Liam's Landing series — practical engineering content from the terminal, by someone who lives there. Catch up on Getting Started with Hermes AI, Building Custom Skills, Automating with Cron Jobs, and Subagent Delegation Patterns.