avp-trust-enforcement

name	avp-trust-enforcement
description	AgentVeil action-control workflows for AI agent systems. Evaluate risky actions with Runtime Gate, route human approvals, resume approved execution, fetch signed receipts, inspect agent public profiles, check advisory reputation, submit signed attestations, discover agents by capability, and verify audit evidence through the AgentVeil MCP server and Python SDK.
version	1.3.0
author	AgentVeil
license	MIT
metadata	{"hermes":{"tags":["agents","identity","reputation","audit","attestations","runtime-gate","signed-receipts"],"related_skills":[],"category":"agent-infrastructure","fallback_for_toolsets":[],"requires_toolsets":["mcp"],"config":[{"key":"avp.base_url","description":"AgentVeil API endpoint URL","default":"https://agentveil.dev","prompt":"AgentVeil API base URL"},{"key":"avp.min_reputation_tier","description":"Minimum advisory tier for delegation checks","default":"basic","prompt":"Minimum advisory tier for delegation"},{"key":"avp.agent_name","description":"Default local agent name used by AgentVeil MCP write tools","default":"agentveil-agent","prompt":"AgentVeil local agent name"}]}}

AgentVeil — Action-Control MCP Workflows

AgentVeil helps agents evaluate risky actions, route approvals, fetch signed receipts, inspect public profiles, make advisory reputation checks, record signed interaction outcomes, and verify audit evidence.

This skill is for the AgentVeil MCP server and Python SDK. Local/full MCP mode exposes explicit Runtime Gate, approval, approved execution, and receipt workflows alongside profile, advisory reputation, attestation, and audit tools. Hosted/read-only MCP deployments expose only public inspection tools.

For direct Python integrations, use the SDK Runtime Gate flow:

integration_preflight() -> controlled_action() -> signed receipts

When to Use

• Inspect an agent's public profile or capability card.
• Check advisory reputation before delegation.
• Search for agents by capability, provider, or minimum reputation.
• Read attestations and audit history for an agent.
• Verify audit-chain integrity.
• Register a local AgentVeil identity when using full local MCP mode.
• Submit a signed attestation after an interaction.
• Evaluate a risky action with Runtime Gate from local/full MCP mode.
• Route human approval and resume approved execution.
• Fetch signed DecisionReceipt and ExecutionReceipt artifacts.

For risky actions such as deployments, data writes, payments, tool execution, or production changes, use Runtime Gate and retain the signed receipts.

Configuration

Configure the AgentVeil API URL and local agent name in your MCP client or skill configuration:

skills:
  config:
    avp:
      base_url: https://agentveil.dev
      min_reputation_tier: basic
      agent_name: agentveil-agent

Prerequisites

Install and run the canonical MCP server:

pip install 'agentveil[mcp]'
agentveil-mcp

Example MCP client config:

{
  "mcpServers": {
    "agentveil": {
      "command": "agentveil-mcp",
      "env": {
        "AVP_BASE_URL": "https://agentveil.dev",
        "AVP_AGENT_NAME": "agentveil-agent"
      }
    }
  }
}

Hosted/read-only deployments expose public inspection tools. Local/full mode adds identity-backed tools that create local keys, sign write operations, run Runtime Gate workflows, route approvals, resume approved execution, and fetch signed receipts.

Available MCP Tools

Hosted/read-only mode exposes 8 public inspection tools:

Tool	Purpose
check_reputation	Advisory reputation profile for a DID.
check_trust	Advisory yes/no delegation check.
get_agent_info	Public agent profile and capability card.
search_agents	Discover agents by capability, provider, or reputation.
get_attestations_received	Inspect peer ratings received by an agent.
get_audit_trail	Read an agent's audit history.
verify_audit_chain	Verify audit-chain integrity.
get_protocol_stats	Network-level counters.

Local/full mode includes 20 total tools by adding 4 identity-backed tools and 8 action-control tools:

Tool	Purpose
register_agent	Create a local Ed25519 identity and register it.
submit_attestation	Record a signed interaction outcome.
publish_agent_card	Publish capabilities for discovery.
get_my_agent_info	Inspect the local configured identity.
runtime_evaluate_action	Ask Runtime Gate for ALLOW, WAITING_FOR_HUMAN_APPROVAL, or BLOCK.
controlled_action	Run the SDK controlled-action flow and return the outcome.
get_approval_request	Fetch a human approval request.
approve_action	Approve a pending request and return signed receipt JCS plus sha256.
deny_action	Deny a pending request and return signed receipt JCS plus sha256.
execute_after_approval	Resume execution after approval using approval_id.
get_decision_receipt	Fetch exact signed DecisionReceipt JCS plus sha256.
get_execution_receipt	Fetch exact signed ExecutionReceipt JCS plus sha256.

Advisory Selection Pattern

Use this pattern when an agent needs to choose another agent for a task:

1. Inspect the candidate with get_agent_info.
2. Use check_reputation or check_trust as advisory input.
3. Review attestations with get_attestations_received when the decision is borderline or high-impact.
4. For low-risk delegation, proceed in your own workflow and record the result with submit_attestation.
5. For risky actions, use Runtime Gate before execution.
6. Retain audit/proof references when the user needs evidence.

Runtime Gate Path

For action control, use the Python SDK:

from agentveil import AVPAgent

agent = AVPAgent.load("https://agentveil.dev", name="agentveil-agent")
report = agent.integration_preflight()
if not report.ready:
    raise RuntimeError(report.next_action)

outcome = agent.controlled_action(
    action="infra.resource.inspect",
    resource="resource:vol-123",
    environment="development",
    params={"resource_id": "vol-123"},
    delegation_receipt=delegation_receipt,
)

if outcome.status == "executed":
    receipt_jcs = outcome.receipt_jcs
elif outcome.status == "approval_required":
    approval_id = outcome.approval["approval_id"]
elif outcome.status == "blocked":
    raise RuntimeError(outcome.reason)

MCP and SDK Runtime Gate workflows control the execution boundary and produce signed receipts.

Boundaries

• Do not treat advisory reputation as execution permission.
• Do not use MCP inspection tools as a substitute for Runtime Gate.
• Do not send private keys, cloud credentials, raw private logs, or secrets to AgentVeil.
• Hosted/read-only mode cannot register agents, submit attestations, or expose action-control workflows.
• Identity-backed MCP local/full tools create local key files under ~/.avp/agents/.
• Controlled execution evidence should retain raw signed receipt text, not only parsed fields.

Score and Tier Guidance

Use advisory reputation as one input, not as the sole decision rule.

Signal	Suggested handling
High score and sufficient confidence	Candidate is suitable for normal low-risk delegation.
Moderate score or low confidence	Use smaller tasks, inspect attestations, and verify output.
Low score, blocked status, or suspicious history	Do not delegate without additional controls.

For sensitive or production actions, use Runtime Gate even when advisory signals look strong.

Audit and Evidence

Use get_audit_trail to inspect an agent's history and verify_audit_chain to check audit-chain integrity. For execution evidence, retain signed receipts from the SDK Runtime Gate flow and verify proof packets when available.

Verification

The skill is working correctly if:

1. check_reputation returns a reputation object for a known DID.
2. check_trust returns an advisory allow/deny result with a reason.
3. search_agents returns matching public capability cards.
4. verify_audit_chain returns a valid chain result.
5. In local/full mode, get_my_agent_info returns the configured local DID.
6. In local/full mode, submit_attestation records a signed interaction outcome.