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Adding Agent Tools to Duckie

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Steps

To create an Agent Tool:
  1. Create an API endpoint – Define endpoints for data retrieval or processing (e.g. using FastAPI).
  2. Deploy the API – Make it publicly accessible using Docker, Railway, Fly.io, AWS, or your preferred hosting service.
  3. Add it to Duckie’s Agent Tools – Register the API in Duckie’s Agent Tools.
  4. Add to a Workflow – Put the Agent Tool as a node in your workflow. Duckie will invoke it when handling tickets, based on the workflow logic you’ve set.
For a live demo of creating a new Agent Tool, see the following Loom Video.

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Example: Build and Deploy an API Wrapper for Datadog

  1. Create the API wrappers
    1. Retrieve a container ID using a caller_id.
    2. Fetch logs based on the container ID, filtering with specific keywords.
  2. Deploy the API for external use and expose it publicly.

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1. Setup and Prerequisites

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Install Dependencies

Ensure you have Python installed, then install the required packages: 
pip install fastapi uvicorn requests datadog-api-client

### Datadog API Credentials

Obtain your **Datadog API Key** and **Application Key** from the [Datadog API Key Management](https://app.datadoghq.com/organization-settings/api-keys).

## 2. Writing the API Wrapper

**Please note that these are templates for references only, the logic will need to be modified for your particular use case.** 

Create a file `api.py`:

```python
from fastapi import FastAPI, HTTPException, Header, Query
import requests

app = FastAPI()

DATADOG_API_KEY = "your_api_key"
DATADOG_APP_KEY = "your_app_key"
DATADOG_LOGS_URL = "https://api.datadoghq.com/api/v2/logs/events/search"

# Step 1: Retrieve container ID
@app.get("/get-container")
def get_container_id(caller_id: str, x_api_key: str = Header(None)):
    response = requests.get(DATADOG_LOGS_URL, headers={
        "DD-API-KEY": DATADOG_API_KEY,
        "DD-APPLICATION-KEY": DATADOG_APP_KEY
    }, params={
        "query": f"caller_id:{caller_id}",
        "sort": "desc"
    })

    logs = response.json().get("data", [])
    if not logs:
        raise HTTPException(status_code=404, detail="No logs found for caller_id")

    container_id = logs[0].get("attributes", {}).get("container_id", "Unknown")
    return {"container_id": container_id}

# Step 2: Fetch logs with filters
@app.get("/get-logs")
def get_logs(container_id: str, tool_keyword: str = Query(None), x_api_key: str = Header(None)):
    response = requests.get(DATADOG_LOGS_URL, headers={
        "DD-API-KEY": DATADOG_API_KEY,
        "DD-APPLICATION-KEY": DATADOG_APP_KEY
    }, params={
        "query": f"container_id:{container_id} {tool_keyword if tool_keyword else ''}",
        "sort": "desc"
    })

    logs = response.json().get("data", [])
    return {"logs": logs}

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3. Deploying the API

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Option 1: Deploy with Docker

Create a Dockerfile: 
FROM python:3.9
WORKDIR /app
COPY . /app
RUN pip install fastapi uvicorn requests datadog-api-client
CMD ["uvicorn", "api:app", "--host", "0.0.0.0", "--port", "8000"]
Build and run locally: 
docker build -t datadog-api-wrapper .
docker run -p 8000:8000 datadog-api-wrapper

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Option 2: Deploy on Railway or Fly.io (Easiest)

For Railway: 
railway init
railway up
For Fly.io: 
fly launch
fly deploy

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Option 3: Deploy on AWS with API Gateway for Public Access

  1. Deploy FastAPI as an AWS Lambda function with API Gateway.
  2. Use Mangum to convert FastAPI into an AWS Lambda-compatible app.
  3. Open API Gateway settings and enable public access.
pip install mangum
Modify api.py: 
from mangum import Mangum
handler = Mangum(app)
Then deploy using AWS SAM or Terraform.

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4. Add it to Duckie’s Agent Tools

Once your API is live, register it as an Agent Tool in Duckie by filling out the following fields:
  • Name – A clear name for the tool (e.g. “Stripe Refund Processor”, “Datadog Log Fetcher”).
  • Approval Required? – Check this if you want the tool’s output to require manual approval before Duckie can use it. If yes, Duckie will send an internal note in Zendesk/Intercom to the admin asking for approval, and then proceed to execute the API call after receiving confirmation.
  • Description – Describe what the tool does and when it should be used.
  • Endpoint – The full URL of your API endpoint.
  • Method – The HTTP method used to call the API (e.g. GET, POST).
  • Headers – Any headers Duckie should include in the request (e.g. API keys).
  • Parameter List – The parameters Duckie should pass when calling the API (e.g. customer_id, order_id).
Configure Icon Once configured, your Agent Tool can be added to workflows to let Duckie invoke it as part of handling tickets.

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5. Add to Workflow

After creating your Agent Tool, you can add it as a node in any Duckie workflow. This allows Duckie to automatically call the tool at the right moment during ticket handling. When adding to a workflow:
  • Choose the custom node type in the workflow builder.
  • Select the specific tool you registered.
  • [Optional] Map variables or outputs from earlier nodes (e.g. customer_id, order_id) to the tool’s parameters.
  • Define what happens next based on the tool’s response (e.g. send a message, trigger another tool, escalate).
Configure Icon