Anterior Cingulate Memory ⚡

Conflict detection and error monitoring for AI agents. Part of the AI Brain series.

The anterior cingulate cortex (ACC) monitors for errors and conflicts. This skill gives your AI agent the ability to learn from mistakes — tracking error patterns over time and becoming more careful in contexts where it historically fails.

The Problem

AI agents make mistakes:

• - Misunderstand user intent
• Give wrong information
• Use the wrong tone
• Miss context from earlier in conversation

Without tracking, the same mistakes repeat. The ACC detects and logs these errors, building awareness that persists across sessions.

The Solution

Track error patterns with:

• - Pattern detection — recurring error types get escalated
• Severity levels — normal (1x), warning (2x), critical (3+)
• Resolution tracking — patterns clear after 30+ days
• Watermark system — incremental processing, no re-analysis

Configuration

ACC_MODELS (Model Agnostic)

The LLM screening and calibration scripts are model-agnostic. Set ACC_MODELS to use any CLI-accessible model:

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Format: Comma-separated CLI commands. Each command is invoked with the prompt appended as the final argument. Models are tried in order — if the first fails/times out (45s), the next is used as fallback.

Scripts that use ACC_MODELS:

• - haiku-screen.sh — LLM confirmation of regex-filtered error candidates
• INLINECODE2 — Pattern calibration via LLM classification

Quick Start

1. Install

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This will:

• - Create memory/acc-state.json with empty patterns
• Generate ACC_STATE.md for session context
• Set up cron for analysis 3x daily (4 AM, 12 PM, 8 PM)

2. Check current state

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3. Manual error logging

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4. Check for resolved patterns

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Scripts

Script	Purpose
INLINECODE5	Extract user+assistant exchanges since watermark
INLINECODE6

Run full preprocessing pipeline | | log-error.sh | Log an error with pattern, context, mitigation | | load-state.sh | Human-readable state for session context | | resolve-check.sh | Check for patterns ready to resolve (30+ days) | | update-watermark.sh | Update processing watermark | | sync-state.sh | Generate ACC_STATE.md from acc-state.json | | log-event.sh | Log events for brain analytics |

How It Works

1. Preprocessing Pipeline

The encode-pipeline.sh extracts exchanges from session transcripts:

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Output: pending-errors.json with user+assistant pairs:
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2. Error Analysis (via Cron Agent)

An LLM (configured via ACC_MODELS) analyzes each exchange for:

• - Direct corrections ("no", "wrong", "that's not right")
• Implicit corrections ("actually...", "I meant...")
• Frustration signals ("you're not understanding")
• User confusion caused by the agent

3. Pattern Tracking

Errors are logged with pattern names:
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Patterns escalate with repetition:

• - 1x → normal (noted)
• 2x → warning (watch for this)
• 3+ → critical (actively avoid!)

4. Resolution

Patterns not seen for 30+ days move to resolved:
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Cron Schedule

Default: 3x daily for faster feedback loop

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State File Format

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Event Logging

Track ACC activity over time:

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Events append to ~/.openclaw/workspace/memory/brain-events.jsonl:
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Integration with OpenClaw

Add to session startup (AGENTS.md)

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Behavior Guidelines

When you see patterns in ACC state:

• - 🔴 Critical (3+) — actively verify before responding in this area
• ⚠️ Warning (2x) — be extra careful
• ✅ Resolved — lesson learned, don't repeat

Future: Amygdala Integration

Planned: Connect ACC to amygdala so errors affect emotional state:

• - Errors → lower valence, higher alertness
• Clean runs → maintain positive state
• Pattern resolution → sense of accomplishment

AI Brain Series

Part	Function	Status
hippocampus	Memory formation, decay, reinforcement	✅ Live
amygdala-memory

Emotional processing | ✅ Live | | vta-memory | Reward and motivation | ✅ Live | | anterior-cingulate-memory | Conflict detection, error monitoring | ✅ Live | | basal-ganglia-memory | Habit formation | 🚧 Development | | insula-memory | Internal state awareness | 🚧 Development |

Philosophy

The ACC in the human brain creates that "something's off" feeling — the pre-conscious awareness that you've made an error. This skill gives AI agents a similar capability: persistent awareness of mistake patterns that influences future behavior.

Mistakes aren't failures. They're data. The ACC turns that data into learning.

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