hive

Here’s a self-improving agent designed to automate the onboarding workflow for our Customer Success team at Acme Corp. The agent’s initial tasks include creating Jira tickets for new user accounts, sending Slack welcome messages, and logging the onboarding status in Google Sheets. It starts by pulling data from our CRM (HubSpot) via API and iteratively improves based on onboarding completion rates (tracked in our internal dashboard) and user feedback collected via Typeform surveys.

First iteration code:
```python
import requests
from datetime import datetime
import pandas as pd

class OnboardingAgent:
    def __init__(self):
        self.crm_api_key = "hsp_abc123xyz"
        self.slack_webhook = "https://hooks.slack.com/services/T123/B456/C789"
        self.jira_api = "https://acme.atlassian.net/rest/api/2/issue"
        self.jira_auth = ("onboarding-agent@acme.com", "api_token_123")
        self.metrics = []

    def fetch_new_users(self):
        url = "https://api.hubapi.com/crm/v3/objects/contacts"
        params = {"limit": 100, "properties": ["email", "firstname", "lastname", "user_type"]}
        headers = {"Authorization": f"Bearer {self.crm_api_key}"}
        response = requests.get(url, headers=headers, params=params)
        return response.json()["results"]

    def create_jira_ticket(self, user):
        payload = {
            "fields": {
                "project": {"key": "ONB"},
                "summary": f"Onboard {user['firstname']} {user['lastname']} ({user['email']})",
                "description": f"New user type: {user['user_type']}. Email: {user['email']}",
                "issuetype": {"name": "Task"}
            }
        }
        response = requests.post(self.jira_api, json=payload, auth=self.jira_auth)
        return response.json()['key']

    def send_slack_welcome(self, user):
        message = {
            "text": f"Welcome to Acme, {user['firstname']}! Your account is being onboarded. Check #onboarding for updates."
        }
        requests.post(self.slack_webhook, json=message)

    def log_metrics(self, user, jira_key, success):
        self.metrics.append({
            "timestamp": datetime.now().isoformat(),
            "user_email": user['email'],
            "jira_ticket": jira_key,
            "success": success
        })
        df = pd.DataFrame(self.metrics)
        df.to_csv("onboarding_metrics.csv", index=False)

    def run(self):
        new_users = self.fetch_new_users()
        for user in new_users:
            try:
                jira_key = self.create_jira_ticket(user)
                self.send_slack_welcome(user)
                self.log_metrics(user, jira_key, True)
            except Exception as e:
                self.log_metrics(user, None, False)
                print(f"Error onboarding {user['email']}: {str(e)}")

agent = OnboardingAgent()
agent.run()
```

Next steps for iteration:
1. Add error handling for API rate limits and retry failed tasks.
2. Integrate with our internal dashboard to pull onboarding completion rates weekly and adjust the agent’s logic (e.g., prioritize high-value users).
3. Use Typeform feedback to refine the Slack welcome message and Jira ticket templates. The agent will self-improve by analyzing which templates lead to faster onboarding completions.