Agent Stability Framework (ASF)

Drift Prevention · Fault Catching · Soul Alignment

Keep your AI agent stable, on-character, and self-correcting across sessions and over time.

What This Solves

Three things kill agent reliability:

1. Drift — Agent gradually reverts to generic training defaults, losing personality
2. Faults — Agent produces broken output, hallucinates, contradicts itself, or fails silently
3. Soul misalignment — Agent technically works but doesn't feel right — lost its essence

ASF addresses all three with one integrated system.

What You Get

• Complete framework documentation (AGENT_STABILITY_FRAMEWORK.md)
• File templates (SOUL.md, BASELINE_EXAMPLES.md, logs)
• System prompt additions ready to paste
• Detection checklists and scoring system
• Works on all models: Claude, GPT, Grok, Gemini, Llama, Mistral

Quick Start

1. Copy all files to your agent's workspace
2. Fill out SOUL.md (who your agent IS)
3. Create BASELINE_EXAMPLES.md (10+ correct responses)
4. Add standing orders + pre-send gate to system prompt
5. Run first audit after 24 hours

Setup time: 45-90 minutes
Daily maintenance: 5 minutes
Tested on: 8+ models across all capability tiers

The Three-Layer Defense

Layer 1: Drift Prevention

• Standing orders (binary rules)
• Pre-send gate (delete triggers)
• Intensifier detection
• Periodic resets

Layer 2: Fault Catching

• 7 fault categories tracked
• Self-check rules before actions
• Fault log + recovery protocol
• Prevents hallucinations, contradictions, silent failures

Layer 3: Soul Alignment

• Catches "technically correct but off-character" responses
• Soul alignment test
• Recovery protocol
• User perception as final sensor

Files Included

• AGENT_STABILITY_FRAMEWORK.md — Complete framework (13KB)
• SOUL_TEMPLATE.md — Identity template
• BASELINE_EXAMPLES_TEMPLATE.md — Response examples template
• DRIFT_LOG_TEMPLATE.md — Drift tracking
• FAULT_LOG_TEMPLATE.md — Fault tracking
• STABILITY_LOG_TEMPLATE.md — Audit scores

Use Cases

• Personal AI assistants that need consistent personality
• Trading bots that must not hallucinate data
• Content generation agents that need stable tone
• Customer service bots that require reliable responses
• Research assistants that must maintain accuracy
• Any agent running 24/7 or across many sessions

Why It Works

1. Binary rules beat judgment calls — "NEVER do X" works consistently
2. Examples anchor identity — Baseline responses are the north star
3. Three failure modes require three defenses — Drift, faults, and soul issues are different
4. Self-correction leverages LLM capabilities — AIs can audit themselves with specific rules
5. Logging creates memory — Patterns become standing orders

Requirements

• OpenClaw workspace
• Any LLM (works across all tested models)
• 30-90 min setup time
• Willingness to document your agent's identity

Credits