agent-self-governance
// 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
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updated:March 4, 2026
SKILL.mdreadonly
SKILL.md Frontmatter
nameagent-self-governance
descriptionSelf-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 →
replayto recover lost context - User correction →
appendBEFORE responding - Pre-compaction flush →
flush-bufferthen write daily memory - During conversation →
buffer-addfor 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
suggestfor optimization tips - Weekly review → run
reportfor 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.