curl --request PUT \
  --url https://{subdomain}.mihu.ai/api/v1/qa-agents/{uuid} \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data @- <<EOF
{
  "name": "Support QA Agent",
  "description": "Evaluates support conversations for empathy and compliance",
  "evaluates_agent_type": "human",
  "is_active": true,
  "is_default": false,
  "evaluation_model": "advanced",
  "strictness_level": 70,
  "confidence_threshold": 85,
  "auto_fail_threshold": 3,
  "language": "English",
  "custom_instructions": "Focus on product knowledge when evaluating technical support calls.",
  "automatic_scoring": true,
  "sentiment_analysis": true,
  "compliance_checking": true,
  "skills": [
    {
      "name": "Empathy",
      "weight": 20,
      "uuid": "9c2f5a44-1b7e-4f4a-9a6c-3d2f8e7b1c0a",
      "description": "Understanding and acknowledging customer concerns",
      "max_score": 10,
      "ai_evaluation_prompt": "Evaluate whether the agent acknowledged the customer's feelings and avoided dismissive language.",
      "is_custom": false,
      "positive_items": [
        {
          "name": "Used the customer's name",
          "points": 5
        }
      ],
      "penalty_items": [
        {
          "name": "Interrupted the customer",
          "penalty_points": -5,
          "is_auto_fail": false
        }
      ]
    }
  ]
}
EOF

import requests

url = "https://{subdomain}.mihu.ai/api/v1/qa-agents/{uuid}"

payload = {
    "name": "Support QA Agent",
    "description": "Evaluates support conversations for empathy and compliance",
    "evaluates_agent_type": "human",
    "is_active": True,
    "is_default": False,
    "evaluation_model": "advanced",
    "strictness_level": 70,
    "confidence_threshold": 85,
    "auto_fail_threshold": 3,
    "language": "English",
    "custom_instructions": "Focus on product knowledge when evaluating technical support calls.",
    "automatic_scoring": True,
    "sentiment_analysis": True,
    "compliance_checking": True,
    "skills": [
        {
            "name": "Empathy",
            "weight": 20,
            "uuid": "9c2f5a44-1b7e-4f4a-9a6c-3d2f8e7b1c0a",
            "description": "Understanding and acknowledging customer concerns",
            "max_score": 10,
            "ai_evaluation_prompt": "Evaluate whether the agent acknowledged the customer's feelings and avoided dismissive language.",
            "is_custom": False,
            "positive_items": [
                {
                    "name": "Used the customer's name",
                    "points": 5
                }
            ],
            "penalty_items": [
                {
                    "name": "Interrupted the customer",
                    "penalty_points": -5,
                    "is_auto_fail": False
                }
            ]
        }
    ]
}
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

response = requests.put(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'PUT',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    name: 'Support QA Agent',
    description: 'Evaluates support conversations for empathy and compliance',
    evaluates_agent_type: 'human',
    is_active: true,
    is_default: false,
    evaluation_model: 'advanced',
    strictness_level: 70,
    confidence_threshold: 85,
    auto_fail_threshold: 3,
    language: 'English',
    custom_instructions: 'Focus on product knowledge when evaluating technical support calls.',
    automatic_scoring: true,
    sentiment_analysis: true,
    compliance_checking: true,
    skills: [
      {
        name: 'Empathy',
        weight: 20,
        uuid: '9c2f5a44-1b7e-4f4a-9a6c-3d2f8e7b1c0a',
        description: 'Understanding and acknowledging customer concerns',
        max_score: 10,
        ai_evaluation_prompt: 'Evaluate whether the agent acknowledged the customer\'s feelings and avoided dismissive language.',
        is_custom: false,
        positive_items: [{name: 'Used the customer\'s name', points: 5}],
        penalty_items: [{name: 'Interrupted the customer', penalty_points: -5, is_auto_fail: false}]
      }
    ]
  })
};

fetch('https://{subdomain}.mihu.ai/api/v1/qa-agents/{uuid}', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://{subdomain}.mihu.ai/api/v1/qa-agents/{uuid}",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "PUT",
  CURLOPT_POSTFIELDS => json_encode([
    'name' => 'Support QA Agent',
    'description' => 'Evaluates support conversations for empathy and compliance',
    'evaluates_agent_type' => 'human',
    'is_active' => true,
    'is_default' => false,
    'evaluation_model' => 'advanced',
    'strictness_level' => 70,
    'confidence_threshold' => 85,
    'auto_fail_threshold' => 3,
    'language' => 'English',
    'custom_instructions' => 'Focus on product knowledge when evaluating technical support calls.',
    'automatic_scoring' => true,
    'sentiment_analysis' => true,
    'compliance_checking' => true,
    'skills' => [
        [
                'name' => 'Empathy',
                'weight' => 20,
                'uuid' => '9c2f5a44-1b7e-4f4a-9a6c-3d2f8e7b1c0a',
                'description' => 'Understanding and acknowledging customer concerns',
                'max_score' => 10,
                'ai_evaluation_prompt' => 'Evaluate whether the agent acknowledged the customer\'s feelings and avoided dismissive language.',
                'is_custom' => false,
                'positive_items' => [
                                [
                                                                'name' => 'Used the customer\'s name',
                                                                'points' => 5
                                ]
                ],
                'penalty_items' => [
                                [
                                                                'name' => 'Interrupted the customer',
                                                                'penalty_points' => -5,
                                                                'is_auto_fail' => false
                                ]
                ]
        ]
    ]
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: Bearer <token>",
    "Content-Type: application/json"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://{subdomain}.mihu.ai/api/v1/qa-agents/{uuid}"

	payload := strings.NewReader("{\n  \"name\": \"Support QA Agent\",\n  \"description\": \"Evaluates support conversations for empathy and compliance\",\n  \"evaluates_agent_type\": \"human\",\n  \"is_active\": true,\n  \"is_default\": false,\n  \"evaluation_model\": \"advanced\",\n  \"strictness_level\": 70,\n  \"confidence_threshold\": 85,\n  \"auto_fail_threshold\": 3,\n  \"language\": \"English\",\n  \"custom_instructions\": \"Focus on product knowledge when evaluating technical support calls.\",\n  \"automatic_scoring\": true,\n  \"sentiment_analysis\": true,\n  \"compliance_checking\": true,\n  \"skills\": [\n    {\n      \"name\": \"Empathy\",\n      \"weight\": 20,\n      \"uuid\": \"9c2f5a44-1b7e-4f4a-9a6c-3d2f8e7b1c0a\",\n      \"description\": \"Understanding and acknowledging customer concerns\",\n      \"max_score\": 10,\n      \"ai_evaluation_prompt\": \"Evaluate whether the agent acknowledged the customer's feelings and avoided dismissive language.\",\n      \"is_custom\": false,\n      \"positive_items\": [\n        {\n          \"name\": \"Used the customer's name\",\n          \"points\": 5\n        }\n      ],\n      \"penalty_items\": [\n        {\n          \"name\": \"Interrupted the customer\",\n          \"penalty_points\": -5,\n          \"is_auto_fail\": false\n        }\n      ]\n    }\n  ]\n}")

	req, _ := http.NewRequest("PUT", url, payload)

	req.Header.Add("Authorization", "Bearer <token>")
	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.put("https://{subdomain}.mihu.ai/api/v1/qa-agents/{uuid}")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"name\": \"Support QA Agent\",\n  \"description\": \"Evaluates support conversations for empathy and compliance\",\n  \"evaluates_agent_type\": \"human\",\n  \"is_active\": true,\n  \"is_default\": false,\n  \"evaluation_model\": \"advanced\",\n  \"strictness_level\": 70,\n  \"confidence_threshold\": 85,\n  \"auto_fail_threshold\": 3,\n  \"language\": \"English\",\n  \"custom_instructions\": \"Focus on product knowledge when evaluating technical support calls.\",\n  \"automatic_scoring\": true,\n  \"sentiment_analysis\": true,\n  \"compliance_checking\": true,\n  \"skills\": [\n    {\n      \"name\": \"Empathy\",\n      \"weight\": 20,\n      \"uuid\": \"9c2f5a44-1b7e-4f4a-9a6c-3d2f8e7b1c0a\",\n      \"description\": \"Understanding and acknowledging customer concerns\",\n      \"max_score\": 10,\n      \"ai_evaluation_prompt\": \"Evaluate whether the agent acknowledged the customer's feelings and avoided dismissive language.\",\n      \"is_custom\": false,\n      \"positive_items\": [\n        {\n          \"name\": \"Used the customer's name\",\n          \"points\": 5\n        }\n      ],\n      \"penalty_items\": [\n        {\n          \"name\": \"Interrupted the customer\",\n          \"penalty_points\": -5,\n          \"is_auto_fail\": false\n        }\n      ]\n    }\n  ]\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://{subdomain}.mihu.ai/api/v1/qa-agents/{uuid}")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Put.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"name\": \"Support QA Agent\",\n  \"description\": \"Evaluates support conversations for empathy and compliance\",\n  \"evaluates_agent_type\": \"human\",\n  \"is_active\": true,\n  \"is_default\": false,\n  \"evaluation_model\": \"advanced\",\n  \"strictness_level\": 70,\n  \"confidence_threshold\": 85,\n  \"auto_fail_threshold\": 3,\n  \"language\": \"English\",\n  \"custom_instructions\": \"Focus on product knowledge when evaluating technical support calls.\",\n  \"automatic_scoring\": true,\n  \"sentiment_analysis\": true,\n  \"compliance_checking\": true,\n  \"skills\": [\n    {\n      \"name\": \"Empathy\",\n      \"weight\": 20,\n      \"uuid\": \"9c2f5a44-1b7e-4f4a-9a6c-3d2f8e7b1c0a\",\n      \"description\": \"Understanding and acknowledging customer concerns\",\n      \"max_score\": 10,\n      \"ai_evaluation_prompt\": \"Evaluate whether the agent acknowledged the customer's feelings and avoided dismissive language.\",\n      \"is_custom\": false,\n      \"positive_items\": [\n        {\n          \"name\": \"Used the customer's name\",\n          \"points\": 5\n        }\n      ],\n      \"penalty_items\": [\n        {\n          \"name\": \"Interrupted the customer\",\n          \"penalty_points\": -5,\n          \"is_auto_fail\": false\n        }\n      ]\n    }\n  ]\n}"

response = http.request(request)
puts response.read_body

{
  "success": true,
  "message": "QA agent updated successfully",
  "data": {
    "uuid": "7e1c9b3a-5d2f-4c8e-b6a1-0f9d8c7b6a5e",
    "name": "Support QA Agent",
    "description": "Evaluates support conversations for empathy and compliance",
    "evaluates_agent_type": "human",
    "is_active": true,
    "is_default": true,
    "evaluation_model": "advanced",
    "strictness_level": 70,
    "confidence_threshold": 85,
    "auto_fail_threshold": 3,
    "language": "English",
    "custom_instructions": "Focus on product knowledge when evaluating technical support calls.",
    "automatic_scoring": true,
    "sentiment_analysis": true,
    "compliance_checking": true,
    "skills": [
      {
        "uuid": "9c2f5a44-1b7e-4f4a-9a6c-3d2f8e7b1c0a",
        "name": "Empathy",
        "description": "Understanding and acknowledging customer concerns",
        "weight": 20,
        "max_score": 10,
        "ai_evaluation_prompt": "Evaluate whether the agent acknowledged the customer's feelings.",
        "positive_items": [
          {
            "name": "Used the customer's name",
            "points": 5
          }
        ],
        "penalty_items": [
          {
            "name": "Interrupted the customer",
            "penalty_points": -5,
            "is_auto_fail": false
          }
        ],
        "order": 2,
        "is_custom": false
      }
    ],
    "total_weight": 100,
    "weights_balanced": true,
    "created_at": "2026-06-04T10:15:30+00:00",
    "updated_at": "2026-06-04T10:15:30+00:00"
  }
}

{
  "success": false,
  "message": "Agent not found",
  "data": []
}

{
  "success": false,
  "message": "Agent not found",
  "data": []
}

{
  "success": false,
  "message": "Agent not found",
  "data": []
}