veroq-agentmesh 0.2.0

VeroQ Shield adapter for OpenAI Agents SDK + Agent Governance Toolkit — output guardrails, trust scoring, content verification

veroq-agentmesh

VeroQ Shield adapter for the Agent Governance Toolkit. Feeds claim-level verification into AgentMesh content governance and trust evaluation.

Two integration points:

• ShieldEvaluator — Runs VeroQ Shield on agent output and maps results to AgentMesh content quality dimensions (accuracy, completeness, consistency). Generates audit entries with verification receipts.
• ShieldActionGuard — Gates actions based on output verification. An agent's trust score is adjusted by Shield confidence before the allow/deny decision.

Install

pip install veroq-agentmesh

Quick Start

from veroq_agentmesh import ShieldEvaluator, ShieldActionGuard

# 1. Evaluate agent output
evaluator = ShieldEvaluator(api_key="vq_...")
report = evaluator.evaluate(
    "NVIDIA reported $22B in Q4 revenue",
    agent_id="did:mesh:researcher",
    content_id="response-001",
)

print(report.trust_score)       # 0.73
print(report.passed)            # False (contradiction found)
print(report.scores)            # {'accuracy': 0.73, 'completeness': 1.0, 'consistency': 0.67}
print(report.audit_entries)     # per-claim receipts

# 2. Feed into policy evaluation context (0-1000 scale)
ctx = report.to_policy_context()
# {'trust_score': 730, 'claims_contradicted': 1, 'receipt_ids': ['r1', 'r2', 'r3'], ...}

# 3. Gate actions with verification
guard = ShieldActionGuard(
    min_trust_score=500,
    sensitive_actions={"publish": 800},
    shield_evaluator=evaluator,
)

result = guard.check_with_verification(
    agent_did="did:mesh:writer",
    agent_trust_score=900,
    action="publish",
    output_text="NVIDIA reported $22B in Q4 revenue",
)
# effective_score = 900 * 0.73 = 657 < 800 -> denied

How It Maps

Shield Output	AgentMesh Dimension	Description
trust_score (0-1)	accuracy	Overall factual accuracy of the output
verifiable / total claims	completeness	Fraction of claims that could be checked
1 - contradicted / total	consistency	Absence of contradictions
Per-claim receipts	Audit trail entries	Cryptographic proof of each verification
trust_score * 1000	Policy context trust_score	0-1000 scale for PolicyEvaluator

With ContentQualityEvaluator

from agent_os.content_governance import (
    ContentQualityEvaluator, ContentQualityRule,
    ContentDimension, QualityGate,
)
from veroq_agentmesh import ShieldEvaluator

# Configure quality gates
quality_eval = ContentQualityEvaluator()
quality_eval.add_rule(ContentQualityRule(
    name="min-accuracy",
    dimension=ContentDimension.ACCURACY,
    threshold=0.8,
    gate=QualityGate.FAIL,
))
quality_eval.add_rule(ContentQualityRule(
    name="min-consistency",
    dimension=ContentDimension.CONSISTENCY,
    threshold=0.9,
    gate=QualityGate.WARN,
))

# Run Shield and map to quality dimensions
shield_eval = ShieldEvaluator(api_key="vq_...")
scores = shield_eval.evaluate_to_quality_scores(agent_output)
dim_scores = {ContentDimension(k): v for k, v in scores.items()}

report = quality_eval.evaluate("agent-1", "resp-1", dim_scores)
print(report.passed)    # True/False
print(report.warnings)  # consistency warnings
print(report.failures)  # accuracy failures

Trust Adjustment Formula

When using ShieldActionGuard.check_with_verification():

effective_score = base_trust_score * shield_trust_score

An agent with trust 800 that produces output with 0.5 accuracy gets an effective score of 400 — below most thresholds. This means even a highly trusted agent gets gated if its output can't be verified.

Components

Component	Purpose
ShieldEvaluator	Runs Shield verification and maps to quality dimensions
ShieldActionGuard	Trust-gated actions with optional output verification
ShieldContentReport	Aggregated quality report with policy context conversion
ShieldAuditEntry	Per-claim verification receipt for audit trail

Links

• VeroQ Shield — pip install veroq
• Agent Governance Toolkit
• Integration template

License

MIT