🚀 Why Multi-Agent Systems? Inspiration for No-Code AI Builders

Multi-agent systems aren’t just AI buzz—they’re game-changing for education, business, and community projects. Here’s what’s possible when you combine Chipp (no-code AI agent builder) and Make (automation platform):

  • Virtual Teaching Assistants: Coordinate personalized help for students using specialized sub-agents (for lectures, predictions, and engagement).
  • Lecture Assistant Agents: Instantly tag, organize, and deliver complex educational content for teachers and learners.
  • Predictive Modeling Agents: Bring data-driven insights into courses, dynamically generating analysis based on historical data.
  • Student Engagement Assistants: Keep learners motivated, involved, and excited with tailored AI-driven interactions.

With these workflows, you can orchestrate a team of AIs that work together—no code required!

🛠 Step-by-Step: Build Your Multi-Agent System with Chipp & Make

Prerequisites

  • Free accounts on Chipp.ai and Make.com
  • An idea or problem to solve (e.g., deliver classroom lectures, analyze data, engage students)
  • Basic understanding of the information your agents will interact with (documents, questions, data sources, etc.)

1. Plan and Define Your Agents (Brainstorm & Role Assignment)

Goal: Map your "team" of AI agents, assigning each a clear, single-purpose job.

Example:

  • Lecture Agent: Answers factual questions and provides educational content.
  • Predictive Agent: Analyzes data, makes predictions, or summarizes trends.
  • Engagement Agent: Checks in with users, asks probing questions, or suggests extra resources.

How-To:

  • Write down your user journey (e.g., “A student asks a question about history, the system delivers a fact, predicts future exam topics, and offers interactive follow-up.”)
  • For each part, assign a single agent: “Who handles this? What’s their role?”

2. Build Each Agent Separately in Chipp

a. Create Your First Agent (e.g., the Lecture Agent)

  • Go to Chipp.ai
  • Click “New Agent”
  • Fill in:
    • Agent Name: (e.g., “LectureBot”)
    • Role/Purpose: “Delivers concise and referenced answers to subject-specific questions.”
    • Prompt/instructions: Be specific (“When someone asks about a historical event, give a 2–3 sentence summary, provide a citation, and an engaging follow-up question.”)
  • Optionally: Add a Knowledge Base (upload lecture notes, docs, or link resources)
  • Test with a question (e.g., “What started World War I?”)

b. Repeat for Other Agents

  • PredictiveBot: “Analyzes grade trends and predicts student progress based on sample data.”
  • EngagementBot: “Keeps students motivated by asking reflection questions and sharing motivational quotes.”

Tip: Each agent should ONLY focus on its unique function.

3. Set Up Your Multi-Agent Workflow in Make

What you’ll do: Stitch your agents into a pipeline/workflow.

a. Connect Chipp Agents with Make

  • In Chipp, for each agent, click “Integrations” or “API” to create an API key/endpoint (copy these for Make)
  • In Make.com, click “Create a new scenario”
  • Add a trigger (e.g., “Watch for new question from user” via webhook, Google Form, or chat widget)

b. Add Chipp Modules to Make Scenario

  • Search for “HTTP” in Make and select “Make a request” (since direct Chipp integration might be via HTTP/Webhook)
  • For each agent:
    • Add a module calling that agent’s endpoint
    • Pass user’s question, or the output of the previous agent as “input” to the next (e.g., user’s question → LectureBot → answer feeds into PredictiveBot)

c. Configure Data Routing and Formatting

  • Use Make’s “Set variable” or “Text aggregator” modules to combine, format, or separate responses
  • Example:
    1. New question received
    2. Route to LectureBot (get answer)
    3. Send same (or modified) query + context to PredictiveBot (get prediction)
    4. Send both outputs (possibly as a single string) to EngagementBot (get motivational follow-up)

d. Output to End User

  • Choose how your final output is delivered (e.g., via chat, email, dashboard, or a Google Doc)
  • In Make, add a module to send this message back to the user (e.g., via webhook response, Gmail, Slack, etc.)

Visual Example:

[User Q] → [Lecture Agent] → [Predictive Agent] → [Engagement Agent] → [Response to User]

4. Test and Refine Your System

  • In your Make scenario, run a test (“Run Once”)
  • Enter a sample question
  • Watch the full sequence:
    • LectureBot reply?
    • Predictive insight delivered?
    • Engagement message added?
  • Check for clarity, timing, and relevance of each piece.
  • Adjust any agent's instructions (in Chipp) or the data flow (in Make) for improvements.

5. Automate and Share

  • Once it works as intended, set your Make scenario to “ON” (live automation).
  • Share your input link with students, colleagues, or test users.
  • Monitor usage (via Chipp chat logs or Make scenario logs) and refine as needed:
    • Are users getting helpful responses?
    • Does each agent add value?
    • Is follow-up engaging?

🔥 Advanced Tips

  • Use Chipp’s “Knowledge Grounding” to keep answers referenced and accurate—especially for education!
  • Add branching in Make: route specific topics to specialized agents.
  • Set up analytics to see which agent contributes the most value.

💡 Key Tips & Insights

  • Tag and organize all agent outputs for easy retrieval—especially with lecture or content-heavy use cases
  • Use Make’s logic tools to route questions or escalate complex queries to human instructors if AI agents can’t handle them
  • Keep engagement agents “human”—add prompts for encouragement, polls, or quick feedback to students
  • Always test your workflow with real end users for best results

🎬 Watch the Full Video

For the complete walkthrough, visual aids, and more inspiration, check out the source video: ▶️ Multi-Agent Systems with Chipp & Make (YouTube)

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