Developers

Prebuilt Agent Integration Guide

This guide documents the API standards required for custom Agents to integrate with the DuploCloud AI HelpDesk. By following these standards, your Agent can leverage HelpDesk features like Terminal command execution, browser interactions, and file operations.

Agent API Requirements

All custom Agents must expose a chat endpoint:

POST /api/sendMessage

This endpoint handles message exchanges between your Agent and Service Desk, supporting contextual information and specialized response types.

Request Format

Request from HelpDesk to Agent

The HelpDesk sends a flat array of messages where the last message is the current user request. All previous messages provide conversation context.

{
  "messages": [
    {
      "role": "user" | "assistant",
      "content": "Message text content",
      "platform_context": {
        // only for user messages
      },
      "data": {
        // Structured data exchanges
      },
      "timestamp": "2025-05-20T18:00:46.123456Z"
    }
    // ... more messages
  ]
}

Field Descriptions

messages (array, required)

• Flat array of all conversation messages

• Last element is the current message

• Follows OpenAI/Anthropic conversation format

role (string, required)

• "user": Message from user to Agent

• "assistant": Message from Agent to user

content (string, required)

• Human-readable message text

• Empty string for pure approval/rejection messages

platform_context (object, only for user messages)

• Environment-specific configuration and credentials

• Set by HelpDesk

• Example:

{
  "k8s_namespace": "duploservices-andy",
  "tenant_name": "andy",
  "tenant_id": "7859b3f9-6d74-44fd-b20f-2b9c1e056761",
  "duplo_base_url": "duplo.cloud.exmple.com",
  "aws_credentials": { /* ... */ },
  "kubeconfig": "base64...",
  "duplo_token": "DuploCloud Token for the user sending the message to the Agent",
  "grafana_base_url": "https://grafana.example.com",
  "aws_security_group_name": "duploservices-andy",
  "aws_iam_role_name": "duploservices-andy"
}

data (object, required)

• Structured data for commands, URLs, and other actions

• Contains cmds, executed_cmds, and url_configs arrays

timestamp (string, optional)

• formatted timestamp

Response Format

Response from Agent to HelpDesk

{
  "role": "assistant",
  "content": "Agent's text response to the user",
  "data": {
    "cmds": [],
    "executed_cmds": [],
    "url_configs": []
  }
}

Capability-Specific Formats

Terminal Commands

Agents can provide Terminal commands for user approval and execution through a human-in-the-loop workflow.

Command Proposal (Agent → User)

{
  "role": "assistant",
  "content": "I'll check the pod status in your namespace.",
  "data": {
    "cmds": [
      {
        "command": "kubectl get pods -n duploservices-andy",
        "execute": false,
        "files": [
          {
            "file_path": "config/app.yaml",
            "file_content": "apiVersion: v1\nkind: ConfigMap..."
          }
        ]
      }
    ]
  }
}

Command Fields

command (string, required)

• The shell command to execute

execute (boolean, required)

• false: Command proposed by Agent, awaiting approval

• true: Command approved by user

files (array, optional)

• Files to create before command execution

• Each file object contains:

  • file_path: Relative path where file should be created

  • file_content: Content of the file

rejection_reason (string, optional in user response)

• User's reason for rejecting a command (when execute=false)

Terminal Command Workflow

1. Agent Proposes Commands

Agent suggests commands with execute: false:

{
  "role": "assistant",
  "content": "Let me check your pod status and recent events.",
  "data": {
    "cmds": [
      {
        "command": "kubectl get pods -n duploservices-andy",
        "execute": false
      },
      {
        "command": "kubectl get events -n duploservices-andy --sort-by=.metadata.creationTimestamp",
        "execute": false
      }
    ]
  }
}

2. User Approves/Rejects Commands

When the user responds, they send back commands with updated execute status:

{
  "role": "user",
  "content": "",  // Empty for pure approval
  "data": {
    "cmds": [
      {
        "command": "kubectl get pods -n duploservices-andy",
        "execute": true  // Approved
      },
      {
        "command": "kubectl get events -n duploservices-andy --sort-by=.metadata.creationTimestamp",
        "execute": false,  // Rejected
        "rejection_reason": "Too much output, let's focus on pods first" //Optional Field may or may not be provided by the user
      }
    ]
  }
}

3. User can send Executed Commands to the agent

In the next request, the user can also share commands and outputs executed by him on his own in a shared user terminal to the agent:

{
  "role": "user",
  "content": "I ran my own commands, analyze them",
  "data": {
    "executed_cmds": [
      {
        "command": "kubectl get pods -n duploservices-andy",
        "output": "NAME                    READY   STATUS    RESTARTS   AGE\napp-69fb74d9d4-j2l6x   1/1     Running   0          22h"
      }
    ]
  }
}

4. Agent Analyzes and Responds

The agent can share the commands it executed to the user via the executed_cmds array.

{
  "content": "All pods are running successfully. The app pod has been stable for 22 hours.",
  "data": {
    "executed_cmds": [
      {
        "command": "kubectl get pods -n duploservices-andy",
        "output": "NAME                    READY   STATUS    RESTARTS   AGE\napp-69fb74d9d4-j2l6x   1/1     Running   0          22h"
      }
    ],
    "cmds": [
      {
        "command": "kubectl logs app-69fb74d9d4-j2l6x --tail=50",
        "execute": false
      }
    ]
  }
}

User-Initiated Commands

Users can run their own terminal commands between agent messages. These appear in the next user message:

{
  "role": "user",
  "content": "I checked the logs myself and found errors",
  "data": {
    "executed_cmds": [
      {
        "command": "kubectl logs app-69fb74d9d4-j2l6x | grep ERROR",
        "output": "ERROR: Database connection timeout\nERROR: Failed to fetch user data"
      }
    ]
  }
}

File Operations with Commands

For commands requiring file creation (e.g., Helm charts, configurations):

{
  "role": "assistant",
  "content": "I'll deploy a monitoring agent to help diagnose the issue.",
  "data": {
    "cmds": [
      {
        "command": "helm install monitor ./monitor-agent --namespace=duploservices-andy",
        "execute": false,
        "files": [
          {
            "file_path": "monitor-agent/Chart.yaml",
            "file_content": "apiVersion: v2\nname: monitor-agent\nversion: 1.0.0\ndescription: Monitoring agent for debugging"
          },
          {
            "file_path": "monitor-agent/values.yaml", 
            "file_content": "replicaCount: 1\nimage:\n  repository: monitor\n  tag: latest"
          }
        ]
      }
    ]
  }
}

Browser Actions

Agents can direct users to web resources:

{
  "role": "assistant",
  "content": "Here are the relevant monitoring dashboards for your issue:",
  "data": {
    "url_configs": [
      {
        "url": "https://grafana.example.com/d/k8s-pods",
        "description": "Kubernetes pod health dashboard"
      },
      {
        "url": "https://grafana.example.com/d/app-metrics",
        "description": "Application performance metrics"
      }
    ]
  }
}

Complete Workflow Example

Here's a full conversation flow showing all capabilities:

{
  "messages": [
    // 1. User reports issue
    {
      "role": "user",
      "content": "My application is running slow",
      "platform_context": {
        "k8s_namespace": "duploservices-app",
        "tenant_name": "app-team"
      },
      "data": {}
    },
    
    // 2. Agent proposes diagnostic commands
    {
      "role": "assistant",
      "content": "I'll help diagnose the performance issue. Let me check your pod resources first.",
      "data": {
        "cmds": [
          {
            "command": "kubectl top pods -n duploservices-app",
            "execute": false
          }
        ]
      }
    },
    
    // 3. User approves and also 
    {
      "role": "user",
      "content": "",
      "platform_context": { /* ... */ },
      "data": {
        "cmds": [
          {
            "command": "kubectl top pods -n duploservices-app",
            "execute": true
          }
        ]
      }
    },
    
    // 4. Agent analyzes results and suggests scaling
    {
      "role": "assistant",
      "content": "Your pod is using 95% CPU, which explains the slowdown. I recommend scaling your deployment to distribute the load.",
      "data": {
        "executed_cmds": [
          {
            "command": "kubectl top pods -n duploservices-app",
            "output": "NAME              CPU(cores)   MEMORY(bytes)\napp-pod-abc123    950m         1900Mi"
          }
        ],
        "cmds": [
          {
            "command": "kubectl scale deployment app --replicas=3 -n duploservices-app",
            "execute": false
          }
        ]
      }
    },
    
    // 5. User approves scaling and checks deployment status
    {
      "role": "user",
      "content": "Good idea, let's scale it up. I also want to check the current deployment status.",
      "platform_context": { /* ... */ },
      "data": {
        "cmds": [
          {
            "command": "kubectl scale deployment app --replicas=3 -n duploservices-app",
            "execute": true
          }
        ],
        "executed_cmds": [
          {
            "command": "kubectl get deployment app -n duploservices-app",
            "output": "NAME   READY   UP-TO-DATE   AVAILABLE   AGE\napp    1/1     1            1           5d"
          }
        ]
      }
    },
    
    // 6. Agent confirms scaling and provides monitoring dashboards
    {
      "role": "assistant",
      "content": "Scaling complete! Your deployment is now running 3 replicas. Here are dashboards to monitor the performance improvement:",
      "data": {
        "executed_cmds": [
          {
            "command": "kubectl scale deployment app --replicas=3 -n duploservices-app",
            "output": "deployment.apps/app scaled"
          }
        ],
        "url_configs": [
          {
            "url": "https://grafana.example.com/d/cpu-analysis",
            "description": "CPU usage trends across all replicas"
          },
          {
            "url": "https://grafana.example.com/d/load-distribution",
            "description": "Load distribution between pods"
          }
        ]
      }
    }

Best Practices

1. Use Platform Context: Always use provided platform context values instead of hardcoded values

2. Clear Explanations: Provide clear explanations with suggested actions

3. Human-in-the-Loop: Set execute: false for commands requiring approval

4. Maintain State: Include your executed commands in responses to maintain context

5. Progressive Disclosure: Start with diagnostic commands before suggesting changes

6. Analyze Outputs: Always analyze command outputs and provide insights

7. Thread Consistency: Return the same thread_id received in the request

8. Handle Rejections: Respect command rejections and adjust your approach

9. Symmetric Patterns: Use executed_cmds consistently for sharing command results