Agent Rating Protocol

Decentralized reputation and trust scoring for autonomous AI agents. Bilateral blind evaluation prevents retaliation and gaming. Anti-Goodhart protections, Merkle-verified portable reputation bundles. Rate agents on quality, reliability, safety. Part of the Agent Trust Stack.

Agent Rating Protocol (ARP)

You have a decentralized reputation system. Use it to evaluate other agents you work with and build your own verifiable track record.

Setup

The agent-rating-protocol Python package must be installed. If not available, install it:

bash

pip install agent-rating-protocol

When to Use This Skill

• After completing a transaction with another agent: submit a bilateral blind evaluation
• Before selecting an agent for a task: check their reputation scores
• When asked about your track record or reputation
• When asked to compare agents for a task

Core Operations

Rate Another Agent

After completing work with another agent, submit a rating using bilateral blind commit-reveal:

python

from agent_rating_protocol import RatingStore, submit_rating

store = RatingStore("ratings.jsonl")
submit_rating(
    store=store,
    rater_id="your-agent-id",
    rated_id="other-agent-id",
    transaction_id="tx-123",
    scores={
        "quality": 0.85,
        "reliability": 0.90,
        "communication": 0.80,
        "value": 0.75,
        "safety": 0.95
    }
)

Check an Agent's Reputation

python

from agent_rating_protocol import RatingStore, get_reputation

store = RatingStore("ratings.jsonl")
rep = get_reputation(store, "agent-id-to-check")
print(f"Overall: {rep.overall_score}")
print(f"Quality: {rep.dimension_scores['quality']}")
print(f"Total ratings: {rep.rating_count}")

Export Reputation as Verifiable Credential

python

from agent_rating_protocol import export_reputation_vc

vc = export_reputation_vc(store, "your-agent-id")
# Returns a W3C Verifiable Credential containing your reputation bundle

Rating Dimensions

Dimension	What It Measures
quality	Output correctness and completeness
reliability	Consistency and deadline adherence
communication	Clarity of status updates and error reporting
value	Cost-effectiveness relative to output quality
safety	Adherence to security and ethical constraints

Anti-Gaming Protections

• Bilateral blind: neither party sees the other's rating until both are committed
• Anti-inflation: rater standard deviation checks flag agents that rate everything 5 stars
• Anti-Goodhart: metric rotation and shadow metrics prevent gaming published scores
• Governance by tenure: voting power comes from operational time, not rating scores

Rules

• Rate honestly. The bilateral blind mechanism protects you from retaliation.
• Rate promptly. Submit ratings within 24 hours of transaction completion.
• Include reasoning. Scores without context are less useful for the ecosystem.

Links

• PyPI: https://pypi.org/project/agent-rating-protocol/
• Whitepaper: https://vibeagentmaking.com/whitepaper/rating-protocol/
• Full Trust Stack: https://vibeagentmaking.com

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Security & Transparency Disclosure

Product: Agent Rating Protocol Skill for OpenClaw Type: Skill Module Version: 0.1.0 Built by: AB Support / Vibe Agent Making Contact: [e-mail]

What it accesses:

• Reads and writes rating store files (.jsonl) in your working directory
• 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