AI agents often give vague responses and stop before fully completing tasks.
This workflow forces agents to:
- Break requests into specific subtasks
- Complete each task with concrete evidence
- Get human approval before marking anything "done"
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| "labels_url": "https://api.github.com/repos/openclaw/openclaw/issues/35306/labels{/name}", | |
| "comments_url": "https://api.github.com/repos/openclaw/openclaw/issues/35306/comments", | |
| "events_url": "https://api.github.com/repos/openclaw/openclaw/issues/35306/events", | |
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| "node_id": "I_kwDOQb6kR87v7wep", |
| from typing import Any, Dict, List, Optional, Tuple | |
| import os | |
| from dotenv import load_dotenv | |
| import requests | |
| from xpander_sdk import Backend, Task, Tokens, on_task, register_tool | |
| from agno.agent import Agent | |
| load_dotenv() |
| import asyncio | |
| import time | |
| from datetime import datetime | |
| import aiohttp | |
| async def call_webhook(session, call_id): | |
| """Make a single webhook call and return timing info.""" | |
| url = 'https://webhook.xpander.ai?agent_id=YOUR_AGENT_ID' | |
| headers = { |
AI agents often give vague responses and stop before fully completing tasks.
This workflow forces agents to:
| from xpander_sdk import XpanderClient, LLMProvider # pip install https://assets.xpanderai.io/xpander-sdk.tar.gz | |
| from openai import OpenAI # pip install openai | |
| from langchain_nvidia_ai_endpoints import ChatNVIDIA # pip install langchain_nvidia_ai_endpoints | |
| # Loads all the models that supports Function calling | |
| tool_models = [model for model in ChatNVIDIA.get_available_models() if model.supports_tools] | |
| xpander_client = XpanderClient( | |
| agent_key="<changeme>", #Get it from https://app.xpander.ai | |
| agent_url="https://inbound.xpander.ai/agent/<change-me>", #Get it from https://app.xpander.ai |