Datadog: Add a prefix and tags - Policy

After creating the policy, add datadog-prefix-and-tags to the policies array of your AI Consumer. The tool examples update an existing AI Consumer with the policy attached: replace the entity configuration in the example with your own, as the update replaces the entire AI Consumer.

kongctl

consumer.yaml

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ai_gateway_consumers:
  - ref: mobile-app-production
    ai_gateway: !lookup {id: !env AI_GATEWAY_ID}
    display_name: Mobile App - Production
    name: mobile-app-production
    type: api-key
    policies:
    - !ref datadog-prefix-and-tags#name

After creating the policy, add datadog-prefix-and-tags to the policies array of your AI Consumer. The tool examples update an existing AI Consumer with the policy attached: replace the entity configuration in the example with your own, as the update replaces the entire AI Consumer.

curl -X PUT https://{region}.api.konghq.com/v1/ai-gateways/{AIGatewayId}/consumers/mobile-app-production \
    --header "accept: application/json" \
    --header "Content-Type: application/json" \
    --header "Authorization: Bearer $KONNECT_TOKEN" \
    --data '
    {
      "display_name": "Mobile App - Production",
      "name": "mobile-app-production",
      "type": "api-key",
      "policies": [
        "datadog-prefix-and-tags"
      ]
    }
    '

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After creating the policy, add datadog-prefix-and-tags to the policies array of your AI Consumer. The tool examples update an existing AI Consumer with the policy attached: replace the entity configuration in the example with your own, as the update replaces the entire AI Consumer.

resource "konnect_ai_gateway_consumer" "my_consumer" {
  display_name = "Mobile App - Production"
  name = "mobile-app-production"
  type = "api-key"
  policies = [konnect_ai_gateway_policy.my_policy.name]


  gateway_id = konnect_ai_gateway.my_ai_gateway.id
}

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This example requires the following variables to be added to your manifest. You can specify values at runtime by setting TF_VAR_name=value.

variable "datadog_host" {
  type = string
}

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After creating the policy, add datadog-prefix-and-tags to the policies array of your AI Model. The tool examples update an existing AI Model with the policy attached: replace the entity configuration in the example with your own, as the update replaces the entire AI Model.

kongctl

model.yaml

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ai_gateway_models:
  - ref: my-gpt-4o
    ai_gateway: !lookup {id: !env AI_GATEWAY_ID}
    display_name: my-gpt-4o
    name: my-gpt-4o
    type: model
    capabilities:
    - generate
    formats:
    - type: openai
    targets:
    - name: gpt-4o
      provider: my-openai-account
      config:
        type: openai
    config:
      route:
        paths:
        - "/v1"
        model:
          body_param: model
          values:
          - my-gpt-4o
      logging:
        payloads: false
    policies:
    - !ref datadog-prefix-and-tags#name

After creating the policy, add datadog-prefix-and-tags to the policies array of your AI Model. The tool examples update an existing AI Model with the policy attached: replace the entity configuration in the example with your own, as the update replaces the entire AI Model.

curl -X PUT https://{region}.api.konghq.com/v1/ai-gateways/{AIGatewayId}/models/my-gpt-4o \
    --header "accept: application/json" \
    --header "Content-Type: application/json" \
    --header "Authorization: Bearer $KONNECT_TOKEN" \
    --data '
    {
      "display_name": "my-gpt-4o",
      "name": "my-gpt-4o",
      "type": "model",
      "capabilities": [
        "generate"
      ],
      "formats": [
        {
          "type": "openai"
        }
      ],
      "policies": [
        "datadog-prefix-and-tags"
      ],
      "targets": [
        {
          "name": "gpt-4o",
          "provider": "my-openai-account",
          "config": {
            "type": "openai"
          }
        }
      ],
      "config": {
        "route": {
          "paths": [
            "/v1"
          ],
          "model": {
            "body_param": "model",
            "values": [
              "my-gpt-4o"
            ]
          }
        },
        "logging": {
          "payloads": false
        }
      }
    }
    '

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After creating the policy, add datadog-prefix-and-tags to the policies array of your AI Model. The tool examples update an existing AI Model with the policy attached: replace the entity configuration in the example with your own, as the update replaces the entire AI Model.

resource "konnect_ai_gateway_model" "my_model" {
  display_name = "my-gpt-4o"
  name = "my-gpt-4o"
  type = "model"
  capabilities = ["generate"]
  formats = [
    {
      type = "openai"
    }  ]
  targets = [
    {
      name = "gpt-4o"
      provider = "my-openai-account"

      config = {
        type = "openai"
      }
    }  ]
  config = jsonencode({

    route = {
      paths = ["/v1"]

      model = {
        body_param = "model"
        values = ["my-gpt-4o"]
      }
    }

    logging = {
      payloads = false
    }
  })
  policies = [konnect_ai_gateway_policy.my_policy.name]


  gateway_id = konnect_ai_gateway.my_ai_gateway.id
}

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This example requires the following variables to be added to your manifest. You can specify values at runtime by setting TF_VAR_name=value.

variable "datadog_host" {
  type = string
}

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After creating the policy, add datadog-prefix-and-tags to the policies array of your AI MCP Server. The tool examples update an existing AI MCP Server with the policy attached: replace the entity configuration in the example with your own, as the update replaces the entire AI MCP Server.

kongctl

mcp_server.yaml

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ai_gateway_mcp_servers:
  - ref: petstore-mcp
    ai_gateway: !lookup {id: !env AI_GATEWAY_ID}
    display_name: Petstore API
    name: petstore-mcp
    type: conversion-listener
    enabled: true
    access:
      acl_attribute_type: consumer
      acls:
        allow: []
      default_tool_acls:
        deny: []
    config:
      url: http://host.docker.internal:8080/api/v3
      route:
        paths:
        - "/petstore"
      logging:
        payloads: false
      server:
        timeout: 60000
    tools:
    - name: get-pets-by-status
      description: Find pets by status
      method: GET
      path: "/petstore/pet/findByStatus"
      parameters:
      - name: status
        in: query
        required: true
        schema:
          type: string
          enum:
          - available
          - pending
          - sold
        description: Status value to filter pets by
    - name: get-pet-by-id
      description: Get pet by ID
      method: GET
      path: "/petstore/pet/{petId}"
      parameters:
      - name: petId
        in: path
        required: true
        schema:
          type: integer
        description: ID of the pet to retrieve
    policies:
    - !ref datadog-prefix-and-tags#name

After creating the policy, add datadog-prefix-and-tags to the policies array of your AI MCP Server. The tool examples update an existing AI MCP Server with the policy attached: replace the entity configuration in the example with your own, as the update replaces the entire AI MCP Server.

curl -X PUT https://{region}.api.konghq.com/v1/ai-gateways/{AIGatewayId}/mcp-servers/petstore-mcp \
    --header "accept: application/json" \
    --header "Content-Type: application/json" \
    --header "Authorization: Bearer $KONNECT_TOKEN" \
    --data '
    {
      "display_name": "Petstore API",
      "name": "petstore-mcp",
      "type": "conversion-listener",
      "enabled": true,
      "policies": [
        "datadog-prefix-and-tags"
      ],
      "access": {
        "acl_attribute_type": "consumer",
        "acls": {
          "allow": []
        },
        "default_tool_acls": {
          "deny": []
        }
      },
      "config": {
        "url": "http://host.docker.internal:8080/api/v3",
        "route": {
          "paths": [
            "/petstore"
          ]
        },
        "logging": {
          "payloads": false
        },
        "server": {
          "timeout": 60000
        }
      },
      "tools": [
        {
          "name": "get-pets-by-status",
          "description": "Find pets by status",
          "method": "GET",
          "path": "/petstore/pet/findByStatus",
          "parameters": [
            {
              "name": "status",
              "in": "query",
              "required": true,
              "schema": {
                "type": "string",
                "enum": [
                  "available",
                  "pending",
                  "sold"
                ]
              },
              "description": "Status value to filter pets by"
            }
          ]
        },
        {
          "name": "get-pet-by-id",
          "description": "Get pet by ID",
          "method": "GET",
          "path": "/petstore/pet/{petId}",
          "parameters": [
            {
              "name": "petId",
              "in": "path",
              "required": true,
              "schema": {
                "type": "integer"
              },
              "description": "ID of the pet to retrieve"
            }
          ]
        }
      ]
    }
    '

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After creating the policy, add datadog-prefix-and-tags to the policies array of your AI MCP Server. The tool examples update an existing AI MCP Server with the policy attached: replace the entity configuration in the example with your own, as the update replaces the entire AI MCP Server.

resource "konnect_ai_gateway_mcp_server" "my_mcp_server" {
  display_name = "Petstore API"
  name = "petstore-mcp"
  type = "conversion-listener"
  enabled = true
  access = {
    acl_attribute_type = "consumer"

    acls = {
      allow = []
    }

    default_tool_acls = {
      deny = []
    }
  }
  tools = [
    {
      name = "get-pets-by-status"
      description = "Find pets by status"
      method = "GET"
      path = "/petstore/pet/findByStatus"
      parameters = [
        {
          name = "status"
          in = "query"
          required = true

          schema = {
            type = "string"
            enum = ["available", "pending", "sold"]
          }
          description = "Status value to filter pets by"
        }      ]
    }, 

    {
      name = "get-pet-by-id"
      description = "Get pet by ID"
      method = "GET"
      path = "/petstore/pet/{petId}"
      parameters = [
        {
          name = "petId"
          in = "path"
          required = true

          schema = {
            type = "integer"
          }
          description = "ID of the pet to retrieve"
        }      ]
    }  ]
  config = jsonencode({
    url = "http://host.docker.internal:8080/api/v3"

    route = {
      paths = ["/petstore"]
    }

    logging = {
      payloads = false
    }

    server = {
      timeout = 60000
    }
  })
  policies = [konnect_ai_gateway_policy.my_policy.name]


  gateway_id = konnect_ai_gateway.my_ai_gateway.id
}

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This example requires the following variables to be added to your manifest. You can specify values at runtime by setting TF_VAR_name=value.

variable "datadog_host" {
  type = string
}

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After creating the policy, add datadog-prefix-and-tags to the policies array of your AI Agent. The tool examples update an existing AI Agent with the policy attached: replace the entity configuration in the example with your own, as the update replaces the entire AI Agent.

kongctl

agent.yaml

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ai_gateway_agents:
  - ref: kongair-flight-booking-agent
    ai_gateway: !lookup {id: !env AI_GATEWAY_ID}
    display_name: KongAir Flight Booking Agent
    name: kongair-flight-booking-agent
    type: a2a
    access:
      acls:
        allow:
        - internal-teams
    config:
      url: https://booking-agent.internal.kongair.com
      route:
        paths:
        - "/kongair-flight-booking"
      logging:
        payloads: false
        max_payload_size: 1048576
    policies:
    - !ref datadog-prefix-and-tags#name

After creating the policy, add datadog-prefix-and-tags to the policies array of your AI Agent. The tool examples update an existing AI Agent with the policy attached: replace the entity configuration in the example with your own, as the update replaces the entire AI Agent.

curl -X PUT https://{region}.api.konghq.com/v1/ai-gateways/{AIGatewayId}/agents/kongair-flight-booking-agent \
    --header "accept: application/json" \
    --header "Content-Type: application/json" \
    --header "Authorization: Bearer $KONNECT_TOKEN" \
    --data '
    {
      "display_name": "KongAir Flight Booking Agent",
      "name": "kongair-flight-booking-agent",
      "type": "a2a",
      "access": {
        "acls": {
          "allow": [
            "internal-teams"
          ]
        }
      },
      "policies": [
        "datadog-prefix-and-tags"
      ],
      "config": {
        "url": "https://booking-agent.internal.kongair.com",
        "route": {
          "paths": [
            "/kongair-flight-booking"
          ]
        },
        "logging": {
          "payloads": false,
          "max_payload_size": 1048576
        }
      }
    }
    '

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After creating the policy, add datadog-prefix-and-tags to the policies array of your AI Agent. The tool examples update an existing AI Agent with the policy attached: replace the entity configuration in the example with your own, as the update replaces the entire AI Agent.

resource "konnect_ai_gateway_agent" "my_agent" {
  display_name = "KongAir Flight Booking Agent"
  name = "kongair-flight-booking-agent"
  type = "a2a"
  access = {

    acls = {
      allow = ["internal-teams"]
    }
  }
  config = jsonencode({
    url = "https://booking-agent.internal.kongair.com"

    route = {
      paths = ["/kongair-flight-booking"]
    }

    logging = {
      payloads = false
      max_payload_size = 1048576
    }
  })
  policies = [konnect_ai_gateway_policy.my_policy.name]


  gateway_id = konnect_ai_gateway.my_ai_gateway.id
}

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This example requires the following variables to be added to your manifest. You can specify values at runtime by setting TF_VAR_name=value.

variable "datadog_host" {
  type = string
}

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Datadog Policy

Add a prefix and tags

Prerequisites

Environment variables

Set up the policy

Attach the policy

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