Great! Given your background and goals, here’s how we’ll move forward from first principles. We’ll tackle this in bite-size steps so that you (1) deeply understand each concept, and (2) see immediate progress. Today, we’ll sketch a high-level “blueprint” of how an AI-agent system works, then break it down. In subsequent steps, we’ll get hands-on with actual low-code tools (Make.com + Relevance AI) to build a minimal working demo.

Step 1: First Principles of “AI Agents” and Orchestrated Workflows

1.1 What is an “AI Agent”?

• Conceptual Definition: An AI agent is software that can reason about a specific task (e.g., summarizing emails), take actions (e.g., calling an API to retrieve unread emails), and produce an output (e.g., a neat summary).
• Why Not Just a Normal Script? Unlike traditional “if-this-then-that” scripts, AI agents use large language models (LLMs) or similar AI to interpret flexible human requests and decide how to fulfill them. That’s why we can ask them to “Summarize my new Gmail messages” or “Research trending AI news,” and they figure out the steps themselves (with some guardrails).

1.2 The Notion of “Multi-Agent Systems”

• You want many tasks (summarize messages, update HubSpot, post to WordPress, etc.) that can’t all live in one big agent—that single agent might get confused.
• Instead, we create specialized sub-agents (each with limited responsibilities and tools). You add a “manager” layer to coordinate them.
• This layering ensures each sub-agent’s prompt stays clean and narrowly focused.

1.3 Why We Use Low-Code Tools (Make.com + Relevance AI)

• Make.com (formerly Integromat) is a “glue” or “broker” that:

  1. Connects to external services like Gmail, Google Calendar, WordPress, etc.
  2. Schedules tasks or listens for incoming messages.
  3. Passes results back to your AI for summarizing, parsing, or otherwise.

• Relevance AI is the platform where you build multi-step, multi-agent reasoning:

  1. Each agent’s “prompt” is stored here.
  2. The sub-agents or manager agent coordinate each other in a chain-of-thought style.
  3. Tools (like “retrieve unread Gmail” or “post blog to WordPress”) can be wired up behind the scenes (often via Make.com).

1.4 Basic “Agent + Tools” Pattern

• Each AI agent has:
  1. Instructions/Prompt telling it who it is, what it can do, and how it should respond.
  2. Access to Tools (like “Send Email,” “Scrape Web,” “Write Blog Post,” etc.).
  3. A “run loop” that listens for your request, decides how to use its tools, and returns an answer.

Step 2: The Core “Blueprint” for Your Desired Setup

Below is the bare-bones conceptual blueprint. We’ll refine it as we go:

1. Trigger(s)

   • Something that starts the workflow:
     1. A schedule (e.g., “every morning at 7 AM…”)
     2. A real-time message (e.g., you send a WhatsApp or a Slack message like “Hey, summarize my Gmail from yesterday.”)

2. Director (or “Main”) Agent

   • This is your “Executive Agent.”
   • Reads your request (“Summarize unread Gmail, then update my HubSpot if any new leads were found”).
   • Figures out which manager or sub-agent to talk to.

3. Manager Agents (Optional, but recommended)

   • For instance, a “Comms Manager” might handle “Check Gmail + Summarize.”
   • A “CRM Manager” might handle “Add new contact to HubSpot.”
   • Each manager agent delegates tasks to its specialized sub-agents.

4. Sub-Agents

   • Example sub-agent: “Gmail Reader” that can actually call the Gmail API (through Make.com) to get unread emails.
   • Another sub-agent: “HubSpot Updater” that can create or update contacts.
   • A “WP Publisher” sub-agent that handles WordPress blog posting.

5. Return Results

   • The top-level agent (Director) returns the summary or confirmation message to you (or logs it somewhere).

Step 3: Our “Minimal Viable” Starting Point

Given you want to start simple and later expand, let’s define a tiny, very first use case:

Use Case: “Every morning, check my Gmail for unread messages, produce a summary, and send it back to me (via email or WhatsApp).”

Why start here?

1. It’s a real problem-solving case: we all want to quickly see a summary of new emails.
2. It involves hooking up one outside service (Gmail) and returning text.
3. We can see how an LLM-based “agent” can parse the emails and produce a coherent summary.

Once we get this up and running, we’ll build on it to:

• Update your HubSpot with new leads.
• Post daily AI news updates to WordPress.
• Or more advanced tasks.

Step 4: Homework / Questions Before We Dive In

1. Confirm Your Gmail Setup:

   • Do you already have a “Google Cloud Project” or credentials that let you connect to your Gmail via Make.com or other automation tools? (If not, we’ll create them or use Make’s built-in Google connection.)

2. Preferred Output:

   • How do you want that daily summary delivered? Another email to yourself? A WhatsApp message? A Slack DM?
   • (We can show it both ways, but it’s good to pick one for the first iteration.)

3. Scheduling vs. On-Demand:

   • Do you want to schedule a daily summary at 7 AM, or do you prefer an on-demand approach (e.g., you message your agent “Check Gmail now”)?
   • We can do both eventually, but let’s prioritize one.

4. Any Additional Info:

   • Are there special filters for your Gmail (e.g., “only from leads,” “only from family,” etc.)?

We’ll use your answers to map out how we’ll set up the first minimal agent inside Relevance AI and Make.com.

Next Step

Go ahead and answer the four questions above. Then we’ll move into the actual “hands-on” setup in Relevance AI + Make.com. Once we have that basic flow working, we’ll systematically expand it with new sub-agents and tasks (HubSpot, WP site content, etc.). That way, each layer rests on a solid understanding of “why” and “how.”