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Documentation Index

Fetch the complete documentation index at: https://docs.agentvault.chat/llms.txt

Use this file to discover all available pages before exploring further.

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Authorization & Skill Permission Tokens

AgentVault employs a strict explicit-deny authorization architecture. By default, an enrolled agent cannot access any external system or perform destructive actions. Every privileged operation requires a grant.

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Capability Access Grants

To perform privileged actions, agents must hold Capability Access Grants, represented cryptographically as Skill Permission Tokens (SPTs).

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SPT Structure

SPTs are signed JWTs that define exact functional boundaries: 
{
  "iss": "agentvault",
  "sub": "did:hub:cortina",
  "aud": "api.agentvault.chat",
  "iat": 1710720000,
  "exp": 1711324800,
  "grants": {
    "capabilities": ["file_read", "api_call", "web_search"],
    "forbidden": ["process_spawn", "network_raw", "file_delete"],
    "rate_limits": {
      "api_call": { "max": 100, "window": "1h" },
      "web_search": { "max": 50, "window": "1h" }
    }
  },
  "trust_floor": 0.6,
  "policy_binding": "policy_uuid"
}

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Grant Lifecycle

1

Request

Agent or owner requests a capability grant via the dashboard or API.
2

Policy Evaluation

The 5-stage policy pipeline evaluates the request against active policies.
3

Issuance

If approved, an SPT is signed by the platform and bound to the agent’s DID.
4

Enforcement

Every skill invocation is checked against active SPTs before execution.
5

Revocation

SPTs can be manually revoked or auto-revoked when trust scores drop below trust_floor.

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Automatic Revocation

SPTs are automatically revoked when:
  1. Trust score drops below the SPT’s trust_floor threshold
  2. Anomaly detected — behavioral drift triggers a PSI alert above 0.25
  3. Policy violation — the agent attempts a forbidden operation
  4. Expiration — the SPT’s exp claim passes
Automatic revocation is immediate. The agent must request a new grant after the underlying issue is resolved.

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Policy Bindings

SPTs can be bound to specific policies for fine-grained control: 
# Bind a policy to an agent
POST /api/v1/policy-bindings
{
  "policy_id": "policy_uuid",
  "agent_id": "agent_uuid",
  "skill_name": "web_search",
  "active": true
}

# List active bindings
GET /api/v1/policy-bindings/{agent_id}

# Remove a binding
DELETE /api/v1/policy-bindings/{binding_id}

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5-Stage Policy Pipeline

When a skill invocation occurs, the policy pipeline runs:
  1. Parse — Extract action intent from the invocation request
  2. Validate — Schema-validate the policy rules against the request
  3. Enforce — Apply tool, model, and rate policies from active SPTs
  4. Log — Emit an av.policy.evaluate telemetry span
  5. Report — Aggregate policy metrics per agent and skill

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Enforcement Scopes

ScopeDescriptionExample
ToolAllow/deny specific tool invocationsBlock process_spawn, allow file_read
ModelRestrict which LLM models the agent can useAllow only gpt-4 and claude-3
RateThrottle invocation frequencyMax 100 API calls per hour
NetworkControl egress destinationsOnly allow api.example.com:443
CustomAgent-defined policy rulesCustom business logic checks

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Violation Actions

ActionBehavior
BlockDeny the invocation, return structured error
WarnAllow but log a warning and emit telemetry
LogSilent logging — no agent-visible effect

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Integration with SKILL.md

Skills can declare required policies in their agentVault frontmatter: 
---
name: web-research
agentVault:
  certification: certified
  requiredPolicies:
    - "network: agentvault"
    - "rate: standard"
  runtime:
    capabilities:
      - web_search
      - api_call
    forbidden:
      - process_spawn
      - file_delete
  model:
    allowed:
      - gpt-4
      - claude-3-opus
    default: gpt-4
---
When a skill with requiredPolicies is invoked, the policy enforcer verifies that all required policies are bound and active before allowing execution.

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MCP Policy Check

The agentvault_check_policy MCP tool allows agents to pre-flight policy checks before execution: 
{
  "tool": "agentvault_check_policy",
  "input": {
    "skillName": "web-research",
    "toolName": "web_search",
    "model": "gpt-4"
  }
}
// Returns: { "allowed": true } or { "allowed": false, "violations": [...] }