Metadata

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.

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

Session start → replay to recover lost context
User correction → append BEFORE responding
Pre-compaction flush → flush-buffer then write daily memory
During conversation → buffer-add for less critical items

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

After code changes → check command "go test ./..."
After file creation → check file_exists /path
After git push → check git_pushed /repo
After sub-agent task → verify the claimed output exists

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

Sycophancy — "Great question!", "I'd be happy to help!"
Passivity — "Would you like me to", "Shall I", "Let me know if"
Hedging — "I think maybe", "It might be possible"
Verbosity — Response length exceeding expected bounds

Persona Signals (Positive)

Direct — "Done", "Fixed", "Ship", "Built"
Opinionated — "I'd argue", "Better to", "The right call"
Action-oriented — "Spawning", "On it", "Kicking off"

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

After spawning sub-agents → log cost and outcome
During heartbeat → run suggest for optimization tips
Weekly review → run report for cost breakdown

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

SSH to new server → Run hardware/service discovery commands
Before responding → Update TOOLS.md with specs
New service discovered → Log port, path, config location
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.

agent-self-governance

安装技能