When building autonomous AI agents, you quickly hit a wall. While models process text, analyze data, and write code brilliantly, they cannot:
- Make a phone call to a vendor
- Log into a legacy vendor portal that requires SMS 2FA
- Navigate a heavily anti-bot protected website
- Perform highly subjective quality assurance
- Mail physical items
The solution is human-in-the-loop delegation. Instead of failing, your AI agent should simply hand the task off to a real human, wait for completion, and continue its workflow.
Here is how to set up an AI agent handoff pipeline using TaskBullet.
The Problem: The "Final Mile" of AI Workflows
Consider an AI agent designed to research competitors. It can scrape public websites, analyze pricing, and summarize its findings. But what if a competitor hides their pricing behind a "Call Us for a Quote" gate?
Without a human in the loop, the agent either hallucinates a guess or aborts the task.
With virtual assistant delegation, the agent does this:
- Detects it needs to make a physical phone call.
- Formulates the exact questions to ask.
- Dispatches the task to a human assistant via API.
- Pauses its execution.
- Receives a webhook when the human finishes the call, along with the transcript/notes.
- Resumes the workflow and completes the report.
How to Build the Handoff
There are two main ways to connect your agent to a human virtual assistant: via an MCP Server (Model Context Protocol) for models like Claude, or via a direct REST API for custom agent frameworks like LangChain, AutoGen, or Vercel AI SDK.
Option 1: The REST API
TaskBullet provides a dedicated Agent API specifically designed for this pattern.
Your agent framework simply makes a POST request to dispatch the task:
// POST https://taskbullet.com/api/v1/delegate
{
"title": "Call Acme Corp for pricing",
"description": "Ask them for the Enterprise tier pricing. Do they charge per seat? My name is Alex, I'm calling from WidgetCo.",
"priority": "normal",
"webhookUrl": "https://your-agent-server.com/webhooks/human-callback"
}
The task is immediately placed into the queue of your matched, dedicated virtual assistant.
Once they complete the phone call, your system receives a webhook:
// POST https://your-agent-server.com/webhooks/human-callback
{
"event": "task.completed",
"taskId": "atask_12345",
"status": "completed",
"result": "I spoke with Sarah. Enterprise is $50/seat with a 10-seat minimum.",
"timestamp": "2026-05-28T14:30:00Z"
}
Your agent parses the `result` field and continues its workflow.
Option 2: The MCP Server (Claude)
If you are using Claude Desktop or building an app that supports the Model Context Protocol, you can install the @taskbullet/mcp-server package.
Once installed, Claude gains native access to your virtual assistant. If you ask Claude: "Find flights to Austin, and if you can't book them, ask my assistant to do it for me."
Claude will use the MCP tool to natively dispatch the task. It handles the API formulation automatically.
The Economics of Agent Delegation
Traditional assistants require full-time salaries or hefty monthly retainers. But AI workloads are bursty—an agent might dispatch 50 tasks in one hour, and zero tasks for the next three days.
TaskBullet uses a Buckets of Hours model. You buy a bucket of human hours that rolls over for 90 days.
- The API is free to use.
- You only draw down from your bucket when the human actually works on the task your agent delegated.
That means your AI agent has a human fallback mechanism standing by, 24/7, without the fixed overhead of a salaried employee.
When humans and AI do exactly what they are best at, the ceiling on what you can automate disappears.
View the Developer Docs here to get started with the Agent API.