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-protocol

When 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

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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: [email protected]