Agent Justice Protocol (AJP)

SkillAI & models

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.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Agent Justice Protocol (AJP) skill

What this skill tells your AI

The instructions your AI receives, as published by fdu-ins/insurance-skills in Skills/agent-justice-protocol/SKILL.md and read by ahel’s review.

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


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

Signals

GitHub stars
73
Forks
19
Last commit
Jul 2026
Advanced
Catalog kind
skill
Gateway key
agent-justice-protocol
Source
github.com/fdu-ins/insurance-skills