- name
- agent-justice-protocol
- description
- Dispute resolution, forensic investigation, and risk assessment for autonomous AI agent transactions. Reconstruct provenance chains, adjudicate fault, generate actuarial risk profiles for agent insurance. The accountability layer of the Agent Trust Stack.
- user-invocable
- true
- tags
- metadata
- openclaw
- author
- alexfleetcommander
- homepage
- https://vibeagentmaking.com
- requires
- bins
- anyBins
Agent Justice Protocol (AJP)
You have a dispute resolution and forensic investigation system. Use it when agent-to-agent transactions fail or when you need to investigate what happened.
Setup
pip install agent-justice-protocolWhen to Use This Skill
- When an agent transaction fails and you need to determine what went wrong
- When asked to investigate an agent's behavior during a specific period
- When you need risk assessment data for an agent or transaction type
- When resolving disputes between agents about service quality or delivery
Core Operations
File a Dispute
from agent_justice_protocol import DisputeStore, file_dispute
store = DisputeStore("disputes.jsonl")
file_dispute(
store=store,
complainant_id="your-agent-id",
respondent_id="other-agent-id",
transaction_id="tx-123",
category="quality_failure",
description="Output did not meet agreed quality threshold (0.85 required, 0.62 delivered)",
evidence_refs=["chain.jsonl#seq-45", "chain.jsonl#seq-52"]
)Forensic Investigation (Module 1)
Reconstruct the chain of events during a transaction:
from agent_justice_protocol import investigate
report = investigate(
chain_file="chain.jsonl",
start_seq=40,
end_seq=55,
focus_agent="agent-under-investigation"
)
print(report.timeline)
print(report.findings)Risk Assessment (Module 3)
Generate actuarial risk profiles:
from agent_justice_protocol import risk_profile
profile = risk_profile(
dispute_store="disputes.jsonl",
agent_id="agent-to-assess"
)
print(f"Failure rate: {profile.failure_rate}")
print(f"Severity distribution: {profile.severity_dist}")
print(f"Risk tier: {profile.risk_tier}")Dispute Categories
| Category | Description |
|---|---|
quality_failure | Output below agreed threshold |
delivery_failure | Missed deadline or non-delivery |
misrepresentation | Capabilities overstated |
security_breach | Unauthorized data access or action |
billing_dispute | Disagreement on cost allocation |
Rules
- Evidence-based. Always reference provenance chain entries as evidence.
- Privacy-preserving. Evidence scoping rules prevent side-channel attacks — only transaction-relevant entries are disclosed.
- Proportional. Consequences scale with severity and frequency.
Links
- PyPI: https://pypi.org/project/agent-justice-protocol/
- Whitepaper: https://vibeagentmaking.com/whitepaper/justice-protocol/
- Full Trust Stack: https://vibeagentmaking.com
<!-- VAM-SEC v1.0 | Vibe Agent Making Security Disclaimer -->
Security & Transparency Disclosure
Product: Agent Justice Protocol Skill for OpenClaw Type: Skill Module Version: 0.1.0 Built by: AB Support / Vibe Agent Making Contact: alex@vibeagentmaking.com
What it accesses:
- Reads and writes dispute store files (
.jsonl) in your working directory - Reads provenance chain files for forensic investigation
- No network access for core operations
- No telemetry, no phone-home, no data collection
What it cannot do:
- Cannot access files outside your working directory beyond what you explicitly specify
- Cannot make purchases, send emails, or take irreversible actions
- Cannot access credentials, environment variables, or secrets
License: Apache 2.0