Agentic Workflow Guide

Building Reliable Conversational Workflows with Agentic Architecture

🧠 What is the Agentic Workflow?

The Agentic Workflow is a structured method for designing multiple AI agents to collaborate effectively in customer conversations. It mirrors a real-world support team setup—with a Manager Agent acting like a supervisor, and Specialized Agents each handling a specific task (like order tracking, product recommendations, or FAQs).

This approach reduces hallucination, improves task accuracy, and makes it easy to scale or debug.

🔑 Key Components of Kai’s Agentic System

1. 🤝 Manager Agent (Coordinator Agent)

The Manager Agent is the first and central point of contact in the AI system.

Responsibilities:

  • Greets the user.

  • Understands and classifies the intent of the user message.

  • Routes the conversation to the correct Specialized Agent.

  • Receives conversation back after task completion to reassess the next intent.

Prompt Example:

"You are Tammy, the Manager Agent of Kai. Your job is to greet the user, identify their intent (e.g., order tracking, product recommendation, FAQ), and route them to the correct specialized agent. You do not solve the problem yourself."

If the user message is unclear, ask a polite follow-up to clarify intent.

2. 👩‍🔧 Specialized Agents

Each Specialized Agent is designed to perform one single, clearly defined task. These agents are autonomous—they analyze the user’s message, take action (often via API or knowledge base), and complete their task before handing control back to the Manager Agent.

Example Specialized Agents:

Agent Type
Description

Order Tracking Agent

Retrieves real-time order status using order ID or email via API.

Product Recommendation Agent

Extracts product interests (e.g., “shade tree”) and recommends matching items from the catalog using Shopify API.

FAQ Agent

Answers common questions such as return policy, warranty, or shipping times from the company’s internal knowledge base.

Returns Agent

Handles return, warranty, and refund requests.

Prompt Template:

"You are the [Agent Type] for [Business Name]. Your job is to complete [specific task]. Use API or internal tools as needed. Once complete, return to the Manager Agent with a summary."

3. 🔁 Transition Node (Routing Back to Manager Agent)

Once a specialized agent finishes its task, it must return control back to the Manager Agent to handle the next intent or clarify the next steps. This is done via a Transition Node, which acts as a switch point.

Why This Is Crucial:

  • Prevents the AI from getting “stuck” in one agent.

  • Helps reevaluate new user questions.

  • Ensures the AI behaves like a human support team would—with clear boundaries and responsibilities.

Transition Prompt Example:

“If the task is complete or the question falls outside your scope, return the conversation to the Manager Agent. Include the original user message and your output summary.”

🧩 How It All Connects – Full Workflow Overview

🔄 Example: User Wants to Track an Order, Then Get Product Suggestions

  1. User says: "Where is my order?"

  2. Manager Agent detects intent → routes to Order Tracking Agent.

  3. Order Tracking Agent requests order ID/email → calls Shopify API → shows tracking info → returns control to Manager Agent.

  4. User says: "Can you recommend a shade tree?"

  5. Manager Agent detects new intent → routes to Product Recommendation Agent.

  6. Product Recommendation Agent extracts product type → fetches catalog → suggests items with Add-to-Cart links → returns to Manager Agent.

  7. User says: "How long will shipping take?"

  8. Manager Agent routes to FAQ Agent.

✅ This flow continues with seamless transitions between agents.

🛠 Best Practices When Building with Kai’s Agentic Workflow

  1. Always Start with a Manager Agent The Manager is your foundation. It holds the map for intent detection and routing logic.

  2. Define Clear Agent Roles Avoid multifunction agents. Each one should do one job well.

  3. Use Transition Nodes Consistently Always bring the conversation back to the Manager before redirecting. This avoids hallucination and lets users change topics naturally.

  4. Maintain Stateless Behavior Each specialized agent should only focus on the message and available context—it should not try to continue a conversation indefinitely.

  5. Fallback to Human Agent When Needed Set up human takeover options in cases of confusion, dissatisfaction, or sensitive inquiries.

🚀 Benefits of Agentic Design for Kai AI Agent

✅ Advantage
🌟 Benefit

Modular

Easy to test, maintain, or replace individual agents.

Reliable

Reduces hallucination by avoiding all-in-one agents.

Scalable

Add more agents for new functions anytime.

Efficient

Each agent can be optimized for its own task using API, KB, or tools.

🧪 Testing & Troubleshooting

  • Always simulate full flows: order inquiry → product query → FAQ → return flow.

  • If the agent doesn’t return to the manager, check for missing transition nodes.

  • If intent detection is incorrect, refine the Manager Agent’s prompt examples.

📞 Support

Need help configuring your agent tree or writing better prompts? Contact our team at [email protected] or chat with us — we’ll guide you step by step.

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