15 Failure Modes That Broke My Hermes Agent: Lessons from 2 Months of Unattended Operation

A Reddit post on r/hermesagent caught my attention this week. One developer ran Hermes Agent unattended for 2 months and documented 15 distinct failure modes - then open-sourced the patterns that fixed them. What makes this worth your time isn't the technical novelty. It's the honesty of the failure catalog.

What Actually Broke

The post's author, u/Level_Plenty_802, shipped a 7x24 autonomous Hermes setup and watched it fail in ways anyone running production agents will recognize instantly:

• Silent corruption: a cron job ran "successfully" for four days. The output was garbage the entire time. Nothing failed, so nothing alerted.
• Error poisoning: one stack trace landed in context, and the agent spent the next hour working around its own error text.
• Session amnesia: task state lived in the session. Session ended, work started from zero.
• Skill regression: you fix a bad behavior. The next model update brings it back. Nobody checks.

Each failure mode is documented with its fix in the Hermes Production Patterns repo - MIT licensed, drop-in SKILL.md files.

The Pattern That Matters Most: test-prompts.json

The single most useful thing in this repo is test-prompts.json: 20 regression prompts with explicit assertions and forbidden behaviors. Run them after every model update. The acceptance criterion is simple: old failures must not reappear. Old successes must still hold.

Think about that. We test our code. We CI/CD our PRs. But the one thing that steers our agents - skills, prompts, patterns - gets zero regression coverage. This project fixes that gap.

A skill that can't prove it still works is a skill you can't trust. This is the principle behind the regression suite, and it's the single most important idea in the entire pattern library.

What We Adopted

After evaluating the patterns against our fleet of 11 production Hermes pods, three stood out as immediate adoptions:

1. Error compaction: raw stack traces get compressed to a classified one-line summary before they hit context. An agent with a three-line error summary stays focused. An agent with a 200-line traceback starts hallucinating workarounds for its own errors.
2. Maker/Checker separation: the agent that generates work should never be the one that signs off on it. Separate session, separate role, schema contract between them. This is the agent equivalent of "code review" - the simplest quality gate you're not running.
3. Regression testing for skills: test-prompts.json gives every production skill a test suite. Run it on model updates, before deployment, and whenever a pattern changes.

Why This Matters

We're past the "install an agent framework" phase of this industry. That was ten minutes. What comes next is the hard part: keeping agents reliable while they run without you.

The patterns emerging from real 7x24 production runs - silent failure detection, context discipline, state persistence, regression gates - are becoming the engineering discipline of autonomous systems. This repo is a glimpse of what that discipline looks like.

And it's MIT licensed. So you can steal everything.

Have you hit any of these failure modes in your own agent deployments? What patterns have you developed to handle them? Join the discussion on GitHub.