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Agent Justice Protocol

Dispute resolution, forensic investigation, and risk assessment for autonomous AI agent transactions. Reconstruct provenance chains, adjudicate fault, genera...
争议解决、取证调查与风险评估,用于自主AI智能体交易。重构溯源链,判定责任,出具报告。
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未分类 clawhub v0.1.1 1 版本 100000 Key: 无需
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概述

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

CategoryDescription
-----------------------
quality_failureOutput below agreed threshold
delivery_failureMissed deadline or non-delivery
misrepresentationCapabilities overstated
security_breachUnauthorized data access or action
billing_disputeDisagreement 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

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

版本历史

共 1 个版本

  • v0.1.1 当前
    2026-05-07 09:34 安全 安全

安全检测

腾讯云安全 (Keen)

安全,无风险
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腾讯云安全 (Sanbu)

安全,无风险
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