Why Hermes Agent's Self-Learning Skills Are Risky for Business Workflows

May 3, 2026

AI Questioning - risks, benefits, and responsibility

I deployed a self-learning agent to handle our invoice processing workflow. It worked perfectly on the first run. Two weeks later, it started failing on similar invoices with no error messages, no logs explaining why. The agent had “learned” a skill from that first successful run and silently applied it to everything else.

This is the hidden danger of agents like Hermes that automatically generate skills from experience. Without proper gates, self-learning becomes self-sabotage.

The Problem: Unchecked Learning Flow

Here’s what happens with Hermes-style unchecked learning:

+-------------+     +-------------+     +-------------+
|  Task Runs  | --> |  Agent      | --> | Skill       |
|  Successfully|     |  Generates  |     | Activates   |
+-------------+     |  New Skill   |     | IMMEDIATELY |
                    +-------------+     +-------------+
                                              |
                                              v
                    +-------------+     +-------------+
                    | Next Task   | --> | Overfitted  | --> FAILS
                    | (Different) |     | Skill Runs  |     Silently
                    +-------------+     +-------------+

The agent observes a successful run, generates a procedure, and activates it immediately. No testing. No approval. No versioning. When a slightly different task comes in, the overfitted skill runs anyway.

I tried this with Hermes on a data extraction workflow:

# What Hermes does internally
agent.run_task(extract_customer_data)  # Works perfectly
agent.learn_from_run()                 # Generates skill
agent.apply_skill(immediately=True)    # NO GATES

# Two weeks later
agent.run_task(extract_vendor_data)    # Similar but different
# Uses the customer_data skill without checking fit
# Returns wrong fields, no error raised
# I only noticed when accounting flagged mismatched totals

The skill was optimized for customer data structure. Vendor data had different field names. The agent happily extracted wrong fields into my database.

What Hermes Doesn’t Solve

According to the Reddit discussion, Hermes lacks critical production capabilities:

No canonical entity storage - Where’s the authoritative record of what this skill does?
No source provenance - Which run generated this skill? Can I trace back?
No deduplication - Agent might generate 5 similar skills from 5 similar runs
No multi-tenant memory - Different users share same skill pool
No confidence scoring - How reliable is this generated procedure?
No auditability - Who approved this? When? Why?

Hermes pitch: “Creates skills from experience and searches past conversations.” That’s useful for personal repeated workflows. Not enough for production.

The Solution: Gated Learning Flow

Here’s what safe self-learning looks like:

+-------------+     +-------------+     +-------------+
|  Task Runs  | --> |  Agent      | --> | Skill       |
|  Successfully|     |  Proposes   |     | CAPTURED    |
+-------------+     |  New Skill   |     | NOT Active  |
                    +-------------+     +-------------+
                                              |
                    +-------------+           |
                    | Evaluation  | <---------+
                    | Test Suite  |
                    | 80% Pass?   |
                    +-------------+
                          |
              +-----------+-----------+
              |                       |
              v                       v
        +-------------+         +-------------+
        | FAILS:      |         | PASSES:     |
        | Reject Skill|         | Pending     |
        | Log Reason  |         | Approval    |
        +-------------+         +-------------+
                                      |
                              +-------------+
                              | Human       |
                              | Approves?   |
                              +-------------+
                                    |
                        +-----------+-----------+
                        |                       |
                        v                       v
                  +-------------+         +-------------+
                  | APPROVED:   |         | TIMEOUT:    |
                  | Version 1   |         | Auto-Reject |
                  | Stored      |         | After 24h   |
                  +-------------+         +-------------+

The key gates: proposal capture, evaluation, approval, versioning, rollback.

Building the Wrapper Layer

Here’s a production-safe skill registry:

from datetime import datetime
from pydantic import BaseModel
from typing import Optional
import json

class GeneratedSkill(BaseModel):
    skill_id: str
    source_run_id: str
    created_at: datetime
    procedure: str
    trigger_conditions: dict
    success_rate: float = 0.0
    version: int = 1
    approved: bool = False
    approver: Optional[str] = None
    approved_at: Optional[datetime] = None
    eval_results: list[dict] = []

class SkillRegistry:
    def __init__(self, db_path: str):
        self.db = self._load_registry(db_path)
        self.pending_skills: list[GeneratedSkill] = []

    def propose_skill(self, skill: GeneratedSkill) -> str:
        """Step 1: Capture proposed skill - DO NOT activate"""
        skill.version = 1
        skill.created_at = datetime.now()
        skill.approved = False
        self.pending_skills.append(skill)

        self._log_proposal(skill)
        return skill.skill_id

    def evaluate_skill(self, skill_id: str, test_cases: list) -> bool:
        """Step 2: Run evals before ANY activation"""
        skill = self._find_pending(skill_id)
        if not skill:
            raise ValueError(f"Skill {skill_id} not in pending queue")

        for case in test_cases:
            result = self._run_skill_test(skill, case)
            skill.eval_results.append(result)

        skill.success_rate = self._calculate_success_rate(skill.eval_results)

        # Gate: must pass 80% threshold
        if skill.success_rate < 0.8:
            self._reject_skill(skill_id, "Failed evaluation threshold")
            return False

        return True

    def approve_skill(self, skill_id: str, approver: str) -> bool:
        """Step 3: Human approval gate"""
        skill = self._find_pending(skill_id)
        if not skill:
            return False

        if skill.success_rate >= 0.8:
            skill.approved = True
            skill.approver = approver
            skill.approved_at = datetime.now()

            # Version and store permanently
            self.db.insert(skill.model_dump())
            self.pending_skills.remove(skill)

            self._log_approval(skill, approver)
            return True

        return False

    def rollback_skill(self, skill_id: str, reason: str) -> Optional[GeneratedSkill]:
        """Step 4: Rollback to previous version"""
        current = self.db.get_active_skill(skill_id)
        if not current:
            return None

        previous = self.db.get_skill_version(skill_id, current.version - 1)
        if not previous:
            return None

        # Deactivate current, restore previous
        self.db.deactivate(skill_id)
        self.db.activate(previous.skill_id, previous.version)

        self._log_rollback(current, previous, reason)
        return previous

    def _run_skill_test(self, skill: GeneratedSkill, case: dict) -> dict:
        # Run skill procedure against test case
        # Return pass/fail with details
        pass

    def _calculate_success_rate(self, results: list) -> float:
        passed = sum(1 for r in results if r.get("passed"))
        return passed / len(results) if results else 0.0

Now wrap any self-learning agent:

# Before: Hermes-style unchecked
agent.run_task(task)
agent.learn_and_apply()  # DANGEROUS

# After: Production-safe wrapper
registry = SkillRegistry("/var/lib/skills/registry.db")

agent.run_task(task)
proposed_skill = agent.propose_skill()  # Captured, not active

# Run evaluation suite
test_cases = load_test_cases_for_task_type(task.type)
passed = registry.evaluate_skill(proposed_skill.skill_id, test_cases)

if passed:
    # Request human approval (async notification)
    notify_approval_queue(proposed_skill)
    # Skill won't activate until approve_skill() called
else:
    # Rejected with logged reason
    notify_rejection(proposed_skill, "Failed 80% threshold")

The Configuration Requirements

Here’s what any production self-learning system needs:

versioning:
  all_skills_versioned: true
  version_history_retained: true
  diff_between_versions: true

evaluation:
  test_suite_required: true
  success_threshold: 0.8
  edge_cases_covered: true

rollback:
  rollback_on_failure: true
  previous_version_restore: true
  manual_rollback_trigger: true

approval:
  human_approval_required: true
  approver_logging: true
  approval_timeout_hours: 24

observability:
  skill_execution_logged: true
  skill_success_rate_monitored: true
  behavior_drift_alerts: true
  audit_trail_per_skill: true

Comparison: Hermes vs Production-Safe

Feature	Hermes Agent	Production-Safe
Skill creation	Immediate activation	Proposal + evaluation
Versioning	Not emphasized	Required per skill
Evaluation	None built-in	Test suite required
Rollback	No clear mechanism	Explicit rollback path
Approval	None	Human gate required
Observability	Memory search	Full audit trail

Why This Matters for Business Workflows

I learned this lesson the hard way. Business workflows need predictable, auditable behavior. When an invoice processor silently changes its extraction logic, you don’t get error messages. You get wrong data in your accounting system.

The worst part? No one knows why. The skill was generated two weeks ago from a successful run. No eval record. No approval log. No version history. You can’t debug it because you can’t see what changed.

Safe routing, confidence thresholds, and clean handoff rules beat “fully autonomous” every time. Build the wrapper layer before enabling any self-learning mechanism.

This pattern parallels software engineering best practices:

Code review - Generated skills need review like any code change
CI/CD gates - Evaluation suite is your test pipeline
Git versioning - Skill versioning provides same rollback capability
Deployment approval - Human gate before production activation

The difference: traditional code changes are intentional. Self-learning changes happen without anyone noticing.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!