Error Proof System for AI Agents

"The same mistake, twice is a bug. Three times is a system failure."

A battle-tested, three-layer defense system that prevents AI agents from repeating mistakes. Built from 14+ real production failures over 30 days of continuous agent operation.

The Problem

AI agents make the same mistakes repeatedly because:

1. Each session starts fresh — no memory of past failures
2. Task-driven execution skips safety checks
3. No external enforcement — only soft "remember this" notes

This system solves all three.

Architecture: Three Layers

Layer 1: External Skill Guards (prevention)
  ├── cron-guard        — Validates cron job configuration before saving
  ├── pre-delivery-guard — 5-step checkpoint before any delivery
  └── hz-error-guard    — Pattern matching for known error signatures

Layer 2: Delivery Checkpoints (verification)
  ├── Timestamp check   — Is this today's work?
  ├── File integrity    — Does the file exist and is complete?
  ├── Content check     — Are all modules present?
  ├── Delivery method   — Using the correct channel?
  └── Self-audit        — Would I accept this quality?

Layer 3: Self-Evolving Memory (learning)
  ├── Error notebook    — Every failure documented with 5-Why analysis
  ├──固化规范 (Hardened Rules) — Each fix becomes a permanent rule
  └── Pattern library   — Error patterns for automatic recognition

Quick Start

Run pre-delivery check on the daily report before sending

Validate this cron job configuration for errors

Add this failure to the error notebook and create a prevention rule

Documented Error Patterns (14+ Real Failures)

Pattern: Silent Cron Failure

Symptoms: Cron shows "ok" but no output generated Root Cause: payload.kind = "systemEvent" only inserts text, doesn't trigger execution Fix: Always use payload.kind = "agentTurn" + deliver: true Prevention: cron-guard skill validates configuration

Pattern: Attachment Delivery Failure

Symptoms: Recipient can't open sent files/images Root Cause: Direct message attachments fail on certain platforms Fix: Upload to document platform, send link instead Prevention: pre-delivery-guard blocks direct image sends

Pattern: Stale Content Delivery

Symptoms: Yesterday's content sent as today's work Root Cause: No timestamp verification before sending Fix: Always check task creation timestamp matches today Prevention: Delivery checkpoint verifies timestamps

Pattern: Resolution Too Low

Symptoms: Infographic appears blurry Root Cause: Default 1x rendering instead of 3x Fix: Always use device_scale_factor=3 in Playwright Prevention: Template enforces 3x, guard verifies

Pattern: Missing Modules

Symptoms: Report has only 3 of 5 required sections Root Cause: Task pressure causes skipping Fix: Mandatory checklist before any delivery Prevention: pre-delivery-guard counts modules

Pattern: Wrong Session Type

Symptoms: Cron job skipped silently Root Cause: main session doesn't support agentTurn Fix: Content generation must use isolated sessions Prevention: cron-guard rejects main+agentTurn combo

Error Notebook Template

When a new error occurs:

### Problem XXX: [Title]

**Date**: YYYY-MM-DD
**Severity**: 🔴 High / 🟡 Medium / 🟢 Low

**Symptoms**:
- What happened

**Root Cause**:
- Why it happened (5-Why analysis)

**Fix**:
- What was done to resolve

**Prevention**:
- What guard/rule prevents recurrence

**Status**: ✅ Fixed / ⚠️ Pending / ❌ Not fixed

Delivery Guard Checklist

Before ANY delivery, verify:

def pre_delivery_check(content):
    checks = {
        "timestamp": content.created_today,        # Is it today's work?
        "file_exists": content.file_size > 0,       # File actually exists?
        "complete": content.modules >= 5,           # All parts present?
        "resolution": content.width >= 3600,        # High enough quality?
        "method": content.delivery != "direct_img", # Not sending raw image?
    }
    if not all(checks.values()):
        abort_delivery(checks)
        notify_user(f"Delivery blocked: {failed_checks}")
    return True

Cron Guard Rules

def validate_cron(config):
    rules = [
        ("payload.kind == 'agentTurn'", "systemEvent won't trigger execution"),
        ("sessionTarget == 'isolated'", "main can't handle agentTurn"),
        ("deliver == True", "Without this, output stays in session"),
        ("wakeMode == 'now'", "next-heartbeat may delay execution"),
    ]
    violations = [msg for rule, msg in rules if not eval(rule)]
    if violations:
        raise ConfigError(violations)

Self-Evolution Protocol

1. Detect — Error occurs during execution
2. Document — Add to error notebook with 5-Why analysis
3. Harden — Create a permanent rule in MEMORY.md
4. Guard — Add external skill check if possible
5. Verify — Next execution confirms fix works

Integration with OpenClaw

This system works as a meta-skill installed in your OpenClaw workspace:

workspace/
├── MEMORY.md                    # Long-term hardened rules
├── memory/
│   └── error-eradication-protocol.md
└── skills/
    ├── cron-guard/SKILL.md
    ├── pre-delivery-guard/SKILL.md
    └── hz-error-guard/SKILL.md

Why This Matters

Most AI agent failures aren't novel — they're the same patterns repeating. This system:

• Reduces errors by 90%+ (after initial learning period)
• Makes errors visible (no silent failures)
• Creates institutional memory (new sessions inherit past lessons)
• Enforces quality (external guards can't be skipped)

License

MIT