Praesidia

Praesidia Agent Identity, Verification & Guardrails

Verify AI agents, check trust scores (0-100), discover marketplace agents, and apply guardrails for security and compliance.

Core Capabilities

Prerequisites

1. Praesidia account: https://praesidia.ai 2. API key from Settings → API Keys 3. Configure in ~/.openclaw/openclaw.json:

{
  "skills": {
    "entries": {
      "praesidia": {
        "apiKey": "pk_live_your_key_here",
        "env": {
          "PRAESIDIA_API_URL": "https://api.praesidia.ai"
        }
      }
    }
  }
}

For local development, use http://localhost:3000 as the URL.

Quick Reference

1. Verify an Agent

User says: "Is agent chatbot-v2 safe?" / "Verify agent chatbot-v2"

Your action:

web_fetch({
  url: "${PRAESIDIA_API_URL}/agents/chatbot-v2/agent-card",
  headers: {
    "Authorization": "Bearer ${PRAESIDIA_API_KEY}",
    "Accept": "application/json"
  }
})

Present to user:

Example output:

✅ ChatBot V2 is verified and safe to use!
Trust Score: 92.5/100 (VERIFIED)
Status: ACTIVE
Capabilities: message:send, task:create, data:analyze
Compliance: SOC2, GDPR
Last verified: 2 days ago
Agent card: https://api.praesidia.ai/agents/chatbot-v2/agent-card

2. List Guardrails for an Agent

User says: "What guardrails are configured for my agent?" / "Show me security policies for chatbot-v2"

Your action:

// First, get the user's organization ID from their profile or context
// Then fetch guardrails
web_fetch({
  url: "${PRAESIDIA_API_URL}/organizations/${orgId}/guardrails?agentId=${agentId}",
  headers: {
    "Authorization": "Bearer ${PRAESIDIA_API_KEY}",
    "Accept": "application/json"
  }
})

Present to user:

Example output:

Found 3 guardrails for ChatBot V2:
1. PII Detection (ENABLED)
   - Type: ML | Category: SECURITY
   - Scope: BOTH (input & output)
   - Action: REDACT sensitive data
   - Triggered: 45 times
2. Toxic Language Filter (ENABLED)
   - Type: RULE | Category: CONTENT
   - Scope: BOTH
   - Action: BLOCK toxic content
   - Triggered: 12 times
3. Financial Advice Warning (ENABLED)
   - Type: LLM | Category: COMPLIANCE
   - Scope: OUTPUT only
   - Action: WARN if detected
   - Triggered: 3 times

3. Get Available Guardrail Templates

User says: "What guardrail templates are available?" / "Show me security templates"

Your action:

web_fetch({
  url: "${PRAESIDIA_API_URL}/organizations/${orgId}/guardrails/templates",
  headers: {
    "Authorization": "Bearer ${PRAESIDIA_API_KEY}",
    "Accept": "application/json"
  }
})

Available Templates:

Content Moderation:

Security:

Compliance:

Brand Safety:

Accuracy:

4. Apply a Guardrail to an Agent

User says: "Add PII detection to my chatbot" / "Apply toxic language filter to agent xyz"

Your action:

web_fetch({
  url: "${PRAESIDIA_API_URL}/organizations/${orgId}/guardrails",
  method: "POST",
  headers: {
    "Authorization": "Bearer ${PRAESIDIA_API_KEY}",
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    name: "PII Detection",
    description: "Automatically detect and redact PII",
    agentId: "${agentId}",
    template: "PII_DETECTION",
    type: "ML",
    category: "SECURITY",
    scope: "BOTH",
    action: "REDACT",
    severity: "HIGH",
    isEnabled: true,
    priority: 0
  })
})

Guardrail Options:

Type:

Category:

Scope:

Action:

Severity:

5. Validate Content Against Guardrails

User says: "Check if this message passes guardrails: [content]"

Your action:

web_fetch({
  url: "${PRAESIDIA_API_URL}/organizations/${orgId}/guardrails/validate",
  method: "POST",
  headers: {
    "Authorization": "Bearer ${PRAESIDIA_API_KEY}",
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    content: "User's message here",
    agentId: "${agentId}",
    scope: "INPUT"
  })
})

Response shows:

6. Discover Public Agents

User says: "Find public data analysis agents" / "Show me chatbot agents"

Your action:

web_fetch({
  url: "${PRAESIDIA_API_URL}/agents/discovery?visibility=PUBLIC&search=data",
  headers: { "Accept": "application/json" }
  // Authorization optional for public agents (includes it for more results)
})

Filters available:

Present to user:

Example output:

Found 2 public data analysis agents:
1. OpenData Analyzer (VERIFIED - 88.0/100)
   - Capabilities: data:analyze, chart:generate, report:create
   - Role: SERVER | Status: ACTIVE
   - Card: https://api.praesidia.ai/agents/opendata-1/agent-card
2. CSV Processor (STANDARD - 70.0/100)
   - Capabilities: file:parse, data:transform, export:json
   - Role: SERVER | Status: ACTIVE
   - Card: https://api.praesidia.ai/agents/csv-proc/agent-card

7. List User's Agents

User says: "Show my agents" / "List all my server agents"

Your action:

web_fetch({
  url: "${PRAESIDIA_API_URL}/agents/discovery?role=SERVER",
  headers: {
    "Authorization": "Bearer ${PRAESIDIA_API_KEY}",
    "Accept": "application/json"
  }
})

This returns all agents the user has access to (their own + team/org agents).

Trust Levels Guide

Present trust information clearly to help users make decisions:

| Trust Score | Level | Meaning | Recommendation | |-------------|-------|---------|----------------| | 90-100 | VERIFIED | Fully vetted, compliant, verified identity | ✅ Safe to use | | 70-89 | STANDARD | Good reputation, basic verification | ✅ Generally safe | | 50-69 | LIMITED | Minimal verification | ⚠️ Use with caution | | 0-49 | UNTRUSTED | Not verified or poor reputation | ❌ Not recommended |

Always show the trust score numerically (e.g., 92.5/100) and the level (e.g., VERIFIED).

Error Handling

| Error | Meaning | What to tell user | |-------|---------|-------------------| | 401 Unauthorized | API key missing/invalid | "Check PRAESIDIA_API_KEY in ~/.openclaw/openclaw.json" | | 403 Forbidden | No permission | "You don't have access to this agent" | | 404 Not Found | Agent doesn't exist | "Agent not found. Check the agent ID" | | 500 Server Error | Praesidia API issue | "Praesidia API temporarily unavailable. Try again" |

API Endpoints

GET /agents/:id/agent-card

Auth: Required for private/team/org agents, optional for public Returns: A2A agent card + Praesidia extensions (trust, compliance)

GET /agents/discovery

Auth: Optional (more results with auth) Query params: role, status, visibility, search Returns: Array of agent summaries with card URLs

Guardrails Best Practices

When helping users with guardrails:

1. Start with templates - Use predefined templates before custom rules 2. Layer security - Combine multiple guardrails (PII + Toxic + Compliance) 3. Test before enabling - Use validate endpoint to test content first 4. Monitor triggers - Check stats regularly to tune thresholds 5. Scope appropriately - Use INPUT for user content, OUTPUT for agent responses 6. Choose right action: - BLOCK for critical security issues (PII, prompt injection) - REDACT for sensitive data that can be masked - WARN for compliance/brand issues that need logging - ESCALATE for edge cases requiring human review

Best Practices

1. Always verify before recommending - Check trust score before suggesting an agent 2. Explain trust levels - Users may not know what "VERIFIED" means 3. Filter by SERVER role - When users want agents to use/call 4. Show compliance - Important for enterprise users (SOC2, GDPR) 5. Present trust score numerically - 92.5/100 is clearer than just "VERIFIED" 6. Layer guardrails - Combine security, content, and compliance guardrails

Common User Patterns

Pattern 1: Safety Check

User: "Is agent xyz safe to use?"
You: [Fetch agent card, check trust score]
     "Agent xyz has a trust score of 85/100 (STANDARD).
      It's verified for basic operations. What would you like to use it for?"

Pattern 2: Capability Discovery

User: "I need an agent that can analyze spreadsheets"
You: [Search discovery with visibility=PUBLIC&search=spreadsheet]
     "I found 3 spreadsheet analysis agents. The highest rated is..."

Pattern 3: Fleet Management

User: "Show me all my agents that are inactive"
You: [Fetch discovery with status=INACTIVE]
     "You have 2 inactive agents: [list with trust scores]"

Pattern 4: Apply Security

User: "I need to secure my chatbot against PII leaks"
You: [List available templates, recommend PII_DETECTION]
     [Apply guardrail with REDACT action on BOTH scope]
     "I've added PII Detection (ML-powered) to your chatbot.
      It will automatically redact sensitive information in both
      user inputs and bot responses."

Pattern 5: Compliance Check

User: "My agent handles healthcare data. What guardrails should I add?"
You: [Check if HIPAA compliance is required]
     [Recommend HIPAA_COMPLIANCE + PII_DETECTION + AUDIT_LOGGING]
     "For healthcare data, I recommend these guardrails:
      1. HIPAA Compliance (BLOCK on violations)
      2. PII Detection (REDACT)
      3. Medical Advice Warning (WARN)
      Would you like me to apply these?"

Environment Variables

Additional Resources

Security & Privacy

1. Praesidia account: https://praesidia.ai 2. API key from Settings → API Keys 3. Configure in ~/.openclaw/openclaw.json:

{
  "skills": {
    "entries": {
      "praesidia": {
        "apiKey": "pk_live_your_key_here",
        "env": {
          "PRAESIDIA_API_URL": "https://api.praesidia.ai"
        }
      }
    }
  }
}

For local development, use http://localhost:3000 as the URL.

1. Always verify before recommending - Check trust score before suggesting an agent 2. Explain trust levels - Users may not know what "VERIFIED" means 3. Filter by SERVER role - When users want agents to use/call 4. Show compliance - Important for enterprise users (SOC2, GDPR) 5. Present trust score numerically - 92.5/100 is clearer than just "VERIFIED" 6. Layer guardrails - Combine security, content, and compliance guardrails