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

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.