Lesson 4.1 – Integration Planning 101

🎯 Learning Objectives

By the end of this lesson, you will be able to:

• Understand the 3 operational modes of AI Agents

• Identify the key planning steps for successful integration

• Determine how your AI Agent will read/write data across systems

• Map AI interaction modes to specific use cases and workflows

• Plan for human-in-the-loop (HITL) handoff in integrated processes

🧠 Why Integration Planning Matters

Building a great AI Agent is only half the job—delivering value means plugging it into your business so it can do something useful.

Whether it’s answering customer emails, helping employees resolve support tickets faster, or automatically updating records in a CRM, integration is what enables action.

Without integration:

• AI becomes just a search tool.

• It can’t complete tasks or update systems.

• Business impact remains minimal.

⚙️ The Three Modes of AI Agent Operation

Successful AI integration starts with knowing how your agent will operate in your environment. We categorize this into three modes:

🧑‍💻 Mode 1: Copilot

“Give me a smart assistant.”

This is a tool for internal teams—a user-triggered AI that enhances productivity.

Use Cases:

• Internal knowledge search

• Complex document summarization

• Creating reports or answers based on internal data

Integration Characteristics:

• Minimal system integration at first

• Typically read-only access to knowledge bases and data repositories

• Can be embedded in internal portals or apps

• Ideal for early-stage AI programs

Example:

A finance analyst uses an AI Copilot in Slack to pull insights from contracts stored in SharePoint.

💬 Mode 2: Conversational

“Let the AI talk to people on our behalf.”

This mode enables two-way conversations with users via multiple channels.

Use Cases:

• Customer support via chat/SMS

• Appointment reminders via email or phone

• Collecting pre-sales qualification data

Integration Characteristics:

• Requires access to communication channels (SMS, email, phone, live chat)

• Often read+write to CRMs, support systems, or booking systems

• Human-in-the-loop logic may be necessary (e.g., escalating to an agent)

Example:

A real estate agent AI responds to SMS inquiries about listings, schedules viewings, and escalates urgent requests to a human.

🤖 Mode 3: Autonomous

“Let the AI do work without being asked.”

This is where the AI becomes part of the workflow backbone—triggered by events, schedules, or business logic.

Use Cases:

• Automatically generate onboarding emails when a new user signs up

• Process insurance claims submitted via form

• Analyze sales trends and trigger reorders in supply chain

Integration Characteristics:

• Often requires bi-directional integrations

• Frequently relies on tools like n8n for workflow orchestration

• Must account for edge cases, exception handling, and escalation paths

Example:

Every Friday, an autonomous AI agent analyzes timesheet data, sends summaries to managers, and notifies payroll of anomalies.

🔗 Planning for System Integration

For each AI mode, you must plan the systems it connects to and how data flows:

🧭 Integration Planning Steps

1. Define the Mode(s) Will this agent act as a Copilot? Will it talk to customers? Will it perform autonomous tasks?

2. Map the Workflow

   • Where in your process will the AI sit?

   • What actions should it take?

   • What systems does it need to access?

3. Identify System Touchpoints

   • Does it need to read or write data?

   • Will it access APIs, databases, or apps?

4. Plan for Exceptions

   • When should a human take over?

   • What happens when data is missing or incomplete?

5. Design the Data Flow Document how data moves in and out of the AI agent, ensuring privacy, accuracy, and traceability.

6. Select Integration Tools

   • Use n8n for event-driven workflows.

   • Use raia’s native integrations for communication and API-based tasks.

   • Use Copilot for real-time feedback and testing.

👥 Human-in-the-Loop (HITL) Integration

No AI is perfect. That’s why HITL capability is critical—especially in customer-facing or decision-sensitive tasks.

Build in HITL logic when:

• Decisions involve high risk or legal sensitivity

• User experience must be preserved (e.g., when AI is unsure)

• Learning from human feedback is desired

Ways to Implement HITL:

• Confidence threshold triggers (e.g., “Low confidence → escalate to human”)

• Manual override options

• Supervisor review queues

📝 Hands-On Planning Worksheet

✅ Key Takeaways

• Start by defining the AI’s mode of operation—this determines your integration strategy.

• Plan early for system touchpoints: what to read, what to write, and who gets looped in.

• Human-in-the-loop is not optional—it’s essential for trust, escalation, and accuracy.

• Integration isn’t a technical detail—it’s how AI creates real business value.