When to Use Workflows to Design AI Agents in HALO

Introduction

In today’s fast-paced world, repetitive tasks can drain time and energy. That’s where workflows come in. Workflows are a powerful way to map out processes and structure tasks, helping you design effective AI Agents in HALO. With HALO’s agentic AI capability, you can streamline customer interactions, reduce errors, and free up time for more meaningful conversations.

But how do you know when a workflow is the right solution to help design an AI Agent? This article will help you identify the tasks that are perfect for automation and show you how workflows can guide the design of your AI Agents in HALO.

What Are Workflows and How Do They Help in HALO?

Workflows are step-by-step representations of processes that help you organize and structure tasks before building AI Agents in HALO. They allow you to break down complex processes into manageable steps, ensuring your AI Agent is designed to handle tasks efficiently and effectively.

Unlike traditional automation tools, HALO’s agentic AI can handle both structured and unstructured data, making it ideal for tasks that require decision-making or context.

Structured data refers to information that is organized and formatted in a predefined way, such as rows and columns in a database or fields in a CRM system. For example, retrieving a customer’s order number and shipping status from a database is a task that involves structured data.
Unstructured data, on the other hand, is information that doesn’t follow a specific format, such as free-text customer feedback, email content, or chat transcripts. For instance, analyzing a customer’s email to extract their complaint and categorize it into themes (e.g., "delivery delay" or "product defect") involves unstructured data.

Example:
Imagine a customer sends an email saying, "Hi, I ordered a laptop last week, but I haven’t received it yet. Can you check the status for me?"

A traditional automation tool might struggle to interpret this unstructured text and extract the relevant details (e.g., "laptop," "last week," "check status").
HALO’s agentic AI, however, can analyze the email, identify the intent (shipping inquiry), extract key details (product: laptop, timeframe: last week), and retrieve the shipping status from an external system. It can then respond to the customer with a clear update, such as: "Your laptop is scheduled for delivery tomorrow."

By using workflows to design your AI Agent, you can ensure it is equipped to handle both structured tasks (like retrieving data from a database) and unstructured tasks (like interpreting free-text queries) seamlessly.

For example, workflows can help you design AI Agents in HALO to:

Extract key information from emails, chats, or calls.
Retrieve shipping statuses or order details from external systems to assist customers in real time.
Summarize customer feedback into actionable insights.
Update CRM systems with customer data.

Why Use Workflows to Design AI Agents?

Workflows are invaluable when designing AI Agents because they bring clarity, scalability, and consistency to the process. By mapping out tasks step by step, workflows help you identify what your AI Agent needs to do and how it should handle each interaction. This structured approach minimizes errors and ensures your AI Agent delivers accurate, reliable results.

Additionally, workflows make it easier to scale your AI Agent’s capabilities as your business grows. Whether you’re handling an increasing volume of customer inquiries or expanding into new use cases, workflows provide a solid foundation for building AI Agents that can adapt to your needs.

By using workflows, you can save time and resources during the design process, focus on higher-value activities like improving customer satisfaction, and ensure your AI Agent consistently delivers exceptional results.

When to Use Workflows in HALO

Not every task requires a workflow, so how do you know when it’s the right approach? Workflows are particularly useful for tasks that are repetitive, scalable, or require context and consistency.

For example, if a task is performed multiple times a day—such as retrieving shipping statuses for customers—it’s a strong candidate for a workflow. Similarly, if the task volume is increasing and manual handling is no longer feasible, a workflow can help you automate the process and ensure scalability.

Tasks that involve unstructured data, like analyzing customer feedback or interpreting free-text queries, also benefit from workflows. By structuring these tasks clearly, you can ensure your AI Agent handles them accurately and consistently.

Examples of Workflows for AI Agents in HALO

Here are some real-world examples of tasks you can structure with workflows to design effective AI Agents in HALO:

Shipping Status Updates: Design an AI Agent that retrieves the shipping status of a package from external systems and provides the customer with real-time updates via chat, email, or phone.
CRM Updates: Create an AI Agent that extracts customer details from emails or chats and updates your CRM system automatically.
Feedback Categorization: Build an AI Agent that analyzes customer feedback from multiple channels and groups it into themes for actionable insights.

Tips for Evaluating Use Cases

Before designing an AI Agent in HALO, use workflows to evaluate the task:

What is the task? Clearly define the process you want the AI Agent to handle.
What is the desired outcome? Identify the result you want the AI Agent to achieve.
Can HALO handle it? Determine if HALO’s agentic AI can manage the task’s complexity, such as retrieving data from external systems or interpreting unstructured information.

By starting with small, manageable tasks, you can build confidence and gradually scale your AI Agent’s capabilities.

Conclusion

Workflows are an essential tool for designing effective AI Agents in HALO. By mapping out processes, clarifying tasks, and identifying the right use cases, you can ensure your AI Agent is equipped to handle customer interactions seamlessly.

Ready to get started? Check out our next article on preparing to build your first AI Agent with HALO.