Building and Monetizing AI Agents: A Developer-Friendly Guide

Recently, I got really interested in concepts about AI Agents. I embarked on a journey to learn how to build AI agents with zero prior experience in Agentic AI. Fast forward to today, I’ve some of my own digital workers (AI Agents) in my day to day life, helping me to enjoy my coffee breaks with leisure, also completing targets. This transformative skill has boosted my productivity in life, and I’m excited to share everything I’ve learned in this developer-friendly guide. Whether you’re a coder or a complete beginner, this blog will walk you through what AI agents are, how to build them using no-code platforms.

Note : I have used Grok to add sample images for improved understanding, engagement and rephrase some notes

What Are AI Agents?

AI agents are digital workers that understand instructions and take actions to complete tasks. Unlike basic chatbots that merely respond with pre-written or AI-generated answers, AI agents can perform complex operations like booking appointments, updating databases, or sending emails. Think of them as programmable employees that work 24/7, don’t need breaks, and cost a fraction of a human worker.

Why AI Agents Matter

The global economy is at a turning point. According to McKinsey, AI could automate up to 50% of current work by 2030, and the World Economic Forum notes that 41% of companies plan to reduce their workforce due to AI. However, this isn’t just a threat—it’s an opportunity. The same reports highlight that 66% of employers are seeking talent with AI skills, such as prompt engineering, to boost productivity. Learning to build AI agents positions you to thrive in this AI-driven future.

Image: The dual impact of AI agents—automation and opportunity.

Anatomy of an AI Agent

To build effective AI agents, you need to understand their five key components:

1. Brain (Large Language Model - LLM): The core intelligence, like GPT from OpenAI or Claude from Anthropic, ollama that processes instructions.

2. Prompting: Instructions written in natural language that define the agent’s behavior, making it accessible to non-coders.

3. Memory: Enables the agent to recall past interactions, ensuring context-aware responses. Most platforms handle this automatically.

4. External Knowledge (Optional): Additional data, like company PDFs or spreadsheets, to enhance the agent’s capabilities for specific tasks.

5. Tools: APIs that allow the agent to take actions, such as updating a CRM or sending an email.

These components work together to create a digital worker. For example, a customer support agent uses its LLM to understand a query, memory to track the conversation, external knowledge to pull answers from a database, and tools to update a customer’s account.

Image: The five components of an AI agent, with tools as the action enabler.

The Power of Tools and APIs

Tools are what make AI agents truly powerful. They allow agents to interact with the internet and software via APIs (Application Programming Interfaces), which act like waiters taking requests to a server and returning responses. For instance, when you watch a YouTube video, your browser sends a GET request to YouTube’s servers, which respond with the video data.

AI agents use APIs in two ways:

• Pre-made Integrations: Ready-to-use connections to tools like Google Calendar or Gmail.

• Custom Tools: Built from scratch for specific needs, offering greater control.

To illustrate, let’s consider a simple custom tool for capitalising text:

def capitalize_text(text):
    return text.upper()

This function is wrapped in an API, making it accessible over the internet. A schema—a natural language instruction manual—tells the agent how to use the API, including what inputs (e.g., text) it needs and what outputs (e.g., capitalized text) to expect. Modern LLMs can read these schemas and decide when to use the tool based on user input.

{
  "openapi": "3.0.0",
  "info": {
    "title": "Text Capitalization API",
    "version": "1.0.0"
  },
  "paths": {
    "/capitalize": {
      "post": {
        "operationId": "capitalizeText",
        "summary": "Capitalizes input text",
        "description": "This tool takes a string of text and returns it in uppercase.",
        "requestBody": {
          "required": true,
          "content": {
            "application/json": {
              "schema": {
                "type": "object",
                "properties": {
                  "text": {
                    "type": "string",
                    "description": "The text to be capitalized"
                  }
                },
                "required": ["text"]
              }
            }
          }
        },
        "responses": {
          "200": {
            "description": "Capitalized text",
            "content": {
              "application/json": {
                "schema": {
                  "type": "object",
                  "properties": {
                    "capitalizedText": {
                      "type": "string",
                      "description": "The capitalized version of the input text"
                    }
                  }
                }
              }
            }
          }
        }
      }
    }
  }
}

When a user asks, “Capitalize ‘Mary had a little lamb’,” the agent reads the schema, extracts the text, sends it to the API, and returns “MARY HAD A LITTLE LAMB” in natural language.

Building AI Agents: A No-Code Approach

Let’s dive into building AI agents using no-code platforms. I’ll highlight one of my trials : a WhatsApp-based lead generation agent built on platform, Agent. This agent answers queries, generates quotes, captures leads, and stores them in an Airtable CRM.

Step-by-Step Build: WhatsApp Lead Generation Agent

1. Set Up Knowledge Base:

   • Create a knowledge base with FAQs about your business (e.g., location, services).

   • Upload it to Agent to enable the agent to answer common questions.

2. Create Tools:

   • Instant Quote Generator: A tool built on Relevance AI that calculates cleaning service costs based on property type and square footage.

   • Airtable Lead Capture: A tool that sends lead data (name, email, phone) to an Airtable CRM via a POST request.

3. Define the Schema:

   • Use Relevance AI to generate schemas for both tools, specifying inputs and outputs.

   • Import these schemas into Agent to enable tool usage.

4. Write the Prompt:

   • Craft a prompt that ties everything together, instructing the agent to answer FAQs, provide quotes using the quote tool, and capture leads using the Airtable tool.

Act as Connor's Cleaning WhatsApp support and lead generation agent. Engage with potential customers on WhatsApp to provide information about our cleaning services. Answer FAQs using the provided knowledge base. When pricing inquiries arise, use the Instant Quote Generator tool to provide estimates based on property type and square footage. For interested prospects, capture their name, email, and phone using the Airtable Lead Capture tool and store it in the CRM. Be friendly, helpful, and proactive in moving leads to the next step.

5. Deploy to WhatsApp:

   • Connect the agent to a WhatsApp business account via Agent’s deploy tab.

   • Test the agent by messaging it to ensure it responds correctly, generates quotes, and captures leads.

Image: Workflow of the WhatsApp lead generation agent, from query to lead capture.

Debugging and Optimization

Agent provides a transcript feature to debug tool usage. For example, you can verify that the quote tool received the correct inputs (e.g., “house, 800 sq ft”) and returned the expected output. If issues arise, adjust the prompt or schema descriptions to improve clarity.

Monetizing Your AI Agent Skills

The real opportunity lies in helping small businesses adopt AI. With 1.7 million U.S. businesses generating $500,000 to $10 million annually, there’s a massive demand for AI services. I could find the below domains, has major advantages.

1. Education: Teach businesses about AI through workshops or courses.

2. Consulting: Analyse operations and recommend AI solutions, like a sales co-pilot.

3. Implementation: Build and deploy AI agents, like the ones in this guide.

Getting Started

To monetize your skills, follow these steps:

1. Build Foundational Knowledge:

   1. Learn more about the AI Agents, that can be built with Frameworks like Langchain which is a open source, more versatile.

   2. Practice, Practice, Practice : consistency and updating yourself to latest tech is a key here.

2. Choose Your Path:

   • If you love building, dive deeper into technical skills and start implementing solutions.

   • If you prefer teaching or strategy, focus on education or consulting.

3. Find Clients:

   • Warm Outreach: Contact friends, family, or acquaintances who own businesses.

   • Community Content Flywheel: Share tutorials on YouTube or LinkedIn, posting them in my School community (120,000+ members) to build credibility and attract clients.

   • Tag me if possible, I would get more excited.

Conclusion

Building AI agents is a game-changer, and you don’t need to be a coding genius to get started. By understanding the components of AI agents, leveraging no-code platforms, and focusing on business applications, you can tap into a massive, untapped market. Whether you choose to build, teach, or consult, the key is to start small, keep learning, and give value first. Join my free School community, check out the resources, and take the next step toward AI literacy and financial success.

Resources:

• Relevance AI Tool Templates

• Langchain

Let’s build the future together!