Agent Self-Governance

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name: agent-self-governance

description: "Self-governance protocol for autonomous agents: WAL (Write-Ahead Log), VBR (Verify Before Reporting), ADL (Anti-Divergence Limit), VFM (Value-For-Money), and IKL (Infrastructure Knowledge Logging). Use when: (1) receiving a user correction — log it before responding, (2) making an important decision or analysis — log it before continuing, (3) pre-compaction memory flush — flush the working buffer to WAL, (4) session start — replay unapplied WAL entries to restore lost context, (5) any time you want to ensure something survives compaction, (6) before claiming a task is done — verify it, (7) periodic self-check — am I drifting from my persona? (8) cost tracking — was that expensive operation worth it? (9) discovering infrastructure — log hardware/service specs immediately."

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# Agent Self-Governance

Five protocols that prevent agent failure modes: losing context, false completion claims, persona drift, wasteful spending, and infrastructure amnesia.

1. WAL (Write-Ahead Log)

**Rule: Write before you respond.** If something is worth remembering, WAL it first.

| Trigger | Action Type | Example |

|---------|------------|---------|

| User corrects you | `correction` | "No, use Podman not Docker" |

| Key decision | `decision` | "Using CogVideoX-2B for text-to-video" |

| Important analysis | `analysis` | "WAL patterns should be core infra not skills" |

| State change | `state_change` | "GPU server SSH key auth configured" |

# Write before responding
python3 scripts/wal.py append <agent_id> correction "Use Podman not Docker"

# Working buffer (batch, flush before compaction)
python3 scripts/wal.py buffer-add <agent_id> decision "Some decision"
python3 scripts/wal.py flush-buffer <agent_id>

# Session start: replay lost context
python3 scripts/wal.py replay <agent_id>

# After incorporating a replayed entry
python3 scripts/wal.py mark-applied <agent_id> <entry_id>

# Maintenance
python3 scripts/wal.py status <agent_id>
python3 scripts/wal.py prune <agent_id> --keep 50

Integration Points

2. VBR (Verify Before Reporting)

**Rule: Don't say "done" until verified.** Run a check before claiming completion.

# Verify a file exists
python3 scripts/vbr.py check task123 file_exists /path/to/output.py

# Verify a file was recently modified
python3 scripts/vbr.py check task123 file_changed /path/to/file.go

# Verify a command succeeds
python3 scripts/vbr.py check task123 command "cd /tmp/repo && go test ./..."

# Verify git is pushed
python3 scripts/vbr.py check task123 git_pushed /tmp/repo

# Log verification result
python3 scripts/vbr.py log <agent_id> task123 true "All tests pass"

# View pass/fail stats
python3 scripts/vbr.py stats <agent_id>

When to VBR

3. ADL (Anti-Divergence Limit)

**Rule: Stay true to your persona.** Track behavioral drift from SOUL.md.

# Analyze a response for anti-patterns
python3 scripts/adl.py analyze "Great question! I'd be happy to help you with that!"

# Log a behavioral observation
python3 scripts/adl.py log <agent_id> anti_sycophancy "Used 'Great question!' in response"
python3 scripts/adl.py log <agent_id> persona_direct "Shipped fix without asking permission"

# Calculate divergence score (0=aligned, 1=fully drifted)
python3 scripts/adl.py score <agent_id>

# Check against threshold
python3 scripts/adl.py check <agent_id> --threshold 0.7

# Reset after recalibration
python3 scripts/adl.py reset <agent_id>

Anti-Patterns Tracked

Persona Signals (Positive)

4. VFM (Value-For-Money)

**Rule: Track cost vs value.** Don't burn premium tokens on budget tasks.

# Log a completed task with cost
python3 scripts/vfm.py log <agent_id> monitoring glm-4.7 37000 0.03 0.8

# Calculate VFM scores
python3 scripts/vfm.py score <agent_id>

# Cost breakdown by model and task
python3 scripts/vfm.py report <agent_id>

# Get optimization suggestions
python3 scripts/vfm.py suggest <agent_id>

Task → Tier Guidelines

| Task Type | Recommended Tier | Models |

|-----------|-----------------|--------|

| Monitoring, formatting, summarization | Budget | GLM, DeepSeek, Haiku |

| Code generation, debugging, creative | Standard | Sonnet, Gemini Pro |

| Architecture, complex analysis | Premium | Opus, Sonnet+thinking |

When to Check VFM

5. IKL (Infrastructure Knowledge Logging)

**Rule: Log infrastructure facts immediately.** When you discover hardware specs, service configs, or network topology, write it down BEFORE continuing.

Triggers

| Discovery Type | Log To | Example |

|----------------|--------|---------|

| Hardware specs | TOOLS.md | "GPU server has 3 GPUs: RTX 3090 + 3080 + 2070 SUPER" |

| Service configs | TOOLS.md | "ComfyUI runs on port 8188, uses /data/ai-stack" |

| Network topology | TOOLS.md | "Pi at 192.168.99.25, GPU server at 10.0.0.44" |

| Credentials/auth | memory/encrypted/ | "SSH key: ~/.ssh/id_ed25519_alexchen" |

| API endpoints | TOOLS.md or skill | "Moltbook API: POST /api/v1/posts" |

Commands to Run on Discovery

# Hardware discovery
nvidia-smi --query-gpu=index,name,memory.total --format=csv
lscpu | grep -E "Model name|CPU\(s\)|Thread"
free -h
df -h

# Service discovery  
systemctl list-units --type=service --state=running
docker ps  # or podman ps
ss -tlnp | grep LISTEN

# Network discovery
ip addr show
cat /etc/hosts

The IKL Protocol

1. **SSH to new server** → Run hardware/service discovery commands

2. **Before responding** → Update TOOLS.md with specs

3. **New service discovered** → Log port, path, config location

4. **Credentials obtained** → Encrypt and store in memory/encrypted/

Anti-Pattern: "I'll Remember"

❌ "The GPU server has 3 GPUs" (only in conversation)

✅ "The GPU server has 3 GPUs" → Update TOOLS.md → then continue

**Memory is limited. Files are permanent. IKL before you forget.**