🔎 Easily Create Your AI Agent for Sales Research: Get the Lead Data You Want in Minutes

I. Introduction to AI Agent Development

In the first lesson of AI Agent section, we got familiar with what AI agents are, why they matter, and how they can play a game-changing role in various fields. Now, with that understanding in hand, it’s time to go from theory to practice.

Building an AI agent may sound like a task for seasoned programmers, but with no-code and less-code tools, it’s something anyone can do! Imagine your own digital assistant running tasks, gathering data, and responding to messages - without needing any serious coding knowledge.

Let’s face it, not everyone has the time or resources to learn complex programming. This is where no-code and low-code platforms step in. These platforms let you create powerful AI tools by simply selecting options, setting rules, and connecting apps - much like putting together puzzle pieces. It’s a game-changer, especially if you:

• Want to experiment with AI without committing to heavy tech skills,

• Need a solution fast without waiting for developers, or

• Have budget constraints but still want the power of automation.

In this next lesson within AI Mastery AZ Course, we’ll walk through everything you need to create an AI agent from scratch using no-code or less-code methods.

So, get ready! By the end, you’ll have built your own AI agent to handle real-world tasks. It’s about making your life easier, saving time, and seeing the power of AI firsthand. Let’s get started!

II. AI Agent Development Frameworks Overview

There are many frameworks that can help create AI agents. These frameworks come with pre-made parts and tools that make creating an AI agent simpler, faster, and way less technical. They provide essential building blocks like data handling, decision-making, and action-taking tools.

Here are some of the best frameworks:

To create an AI Agent using one of these frameworks, you’ll go through these steps:

• Step 1: Set Up Your Workspace and Tools

  • Account Setup: You’ll need to set up accounts on popular no-code platforms like Zapier, Bubble, or SuperAGI’s cloud-based interface.

• Step 2: Create Triggers and Conditions

  • Triggers: Set up triggers to initiate the agent's activity, such as a customer query that activates a response agent.

  • Conditions: Add logical operators (if/then statements) to make these triggers context-sensitive, ensuring actions are relevant.

  • Testing Triggers: Test your triggers to confirm accuracy and make adjustments as necessary.

• Step 3: Defining and Customizing Actions

  • Select Actions: Choose common actions based on the agent’s role, like responding to emails, gathering data, or generating reports.

  • Using Multi-Step Actions: Create workflows that link several actions in sequence, such as “Receive Query → Find Relevant Answer → Respond with Solution.”

  • Integrating Decision Logic: Add decision-making steps to let the agent choose paths based on input (e.g., if a response is satisfactory, end; if not, escalate).

• Step 4: Incorporate Framework-Specific Features

  • LangChain’s Language Model Tools: Integrate LangChain’s components to enable detailed, context-aware responses.

  • AutoGen’s Multi-Agent Conversations: Use AutoGen to create agents that collaborate and handle complex tasks together.

  • PromptAppGPT for UI Generation: Use PromptAppGPT’s tools for auto-generating UIs and integrating text or image generation.

  • SuperAGI’s Vector Databases and Toolkits: Add SuperAGI’s memory functions and toolkits for business-oriented monitoring and performance tracking.

• Step 5: Advanced Workflow Customization and Conditional Paths

  • Develop Branching Logic: Set up conditional branching to allow the agent to make decisions (e.g., if X happens, do Y; else, do Z).

  • Building Complex Workflow Layers: Create multi-layer workflows where different agents manage specific stages of a larger task.

  • Incorporating Delays and Timing: Add strategic delays (e.g., wait for confirmation before another response) for thoughtful processing.

• Bonus: Integrating APIs and External Tools (Less-Code)

  • Understanding API Connectors: Use API connectors to link external applications.

  • Using API with LangChain or AutoGPT: Set up API connections for data collection or analysis tasks.

  • Testing API Integrations: Test these connections to ensure correct data mapping and troubleshoot issues as needed.

• Additional: Optimizing and Scaling Your AI Agent

  • Improving Efficiency: Streamline workflows by removing redundant actions.

  • Enhancing Reliability with AutoGen or SuperAGI: Use AutoGen’s error handling or SuperAGI’s workflow customization for improved performance.

  • Incorporating Memory Storage and Feedback Loops: Use frameworks that support memory and reinforcement, allowing agents to learn over time.

🎁 BONUS: How to Build an AI Agent from Scratch Using Python: Easy Guide for Beginners

As you can see, frameworks like LangChain, AutoGen, and SuperAGI are powerful for building AI agents, but they do require a fair amount of coding experience, especially with Python or JavaScript. So for beginners or if you’re new to coding or looking for an easier way to get started, there are simpler alternatives that still offer robust automation and AI features.

So, for today’s lesson, we’ll dive into tools like n8n and Leap AI - two fantastic options that allow you to set up automated workflows and basic AI agents with minimal technical skills.

These tools are designed with beginners in mind. They provide drag-and-drop interfaces, ready-made integrations, and intuitive setups that let you build workflows and agents without worrying about writing code. They’re perfect if you want to get comfortable with the basics of AI agents before tackling more complex frameworks down the line.

III. Define Your AI Agent's Purpose and Scope

In the previous lesson, we already guide you with detailed steps about this, but to save time, we’ll briefly show them again.

Step 1: Identify the Core Goal of Your AI Agent 

To build an effective AI agent, the first step is deciding exactly what you want it to accomplish. Think about specific tasks that will make your work or daily life easier. The more defined your agent’s purpose is, the more successful it will be in delivering results.

Here are some questions to help narrow down the goal:

• Who will benefit from the agent? Is it for your customers, team, or personal use?

• What problem is the agent solving? Is it responding to repetitive customer questions, managing social media, or organizing data?

• How often does this task need to happen? Is it something that happens daily, on-demand, or in response to specific triggers?

For example, if your goal is to save time on customer service, the agent’s core purpose might be to answer frequently asked questions on your website. If you’re looking to boost social engagement, the agent could handle basic comment replies or alert you to mentions of your brand.

Step 2: Break Down Tasks and Actions the Agent Will Perform 

Once you know the agent’s purpose, break it down into smaller actions. These are the tasks the agent will handle in response to triggers or inputs. Think of these actions as individual steps that, when combined, fulfill the agent’s purpose.

Examples of common tasks:

• Customer Support Agent: Answer specific customer queries, provide account information, escalate complex issues to a human.

• Social Media Agent: Respond to basic comments, like or share specific content, track brand mentions.

• Data Collector: Gather information from websites, organize data into spreadsheets, generate reports based on collected information.

The goal here is to list out every small action your agent needs to perform. This makes setting up the agent much simpler because you’ll know exactly what to include in each workflow step.

Step 3: Define Triggers and Inputs 

Next, you’ll identify the triggers that set off the agent’s tasks. A trigger is simply an event that tells your agent, “It’s time to get to work!” Triggers can be:

• Time-based: These activate the agent at specific intervals (e.g., daily at 9 AM).

• Event-based: These are triggered by specific actions (e.g., a new customer query is received).

• Condition-based: These triggers are based on conditions (e.g., if a customer’s query includes “refund,” the agent activates a specific response).

For example:

• If you’re building a data collection agent, the trigger might be “at 8 AM every Monday” to gather weekly data.

• For a customer service agent, the trigger could be “new support email received” to initiate a response.

Step 4: Gather Necessary Resources 

List out the resources your agent will need to function properly. These might include:

• Data: Any pre-existing data or information the agent will need, such as FAQs for a customer service agent.

• APIs and Integrations: Specific tools or services the agent needs to connect to, such as social media platforms, email systems, or CRMs.

• Templates and Responses: If your agent will be sending messages or emails, prepare templates or sample replies in advance.

For example, a customer support agent will need a list of FAQs to answer common questions. A data-collecting agent may need API access to the platforms from which it gathers information.

Step 5: Set Scope Limitations 

It’s essential to set limitations to avoid scope creep (when the project grows bigger than intended). Decide on what the agent won’t handle and communicate these limitations in the setup. A well-defined scope prevents your agent from doing tasks it’s not designed for, which also reduces errors.

For instance:

• A customer service agent might handle only basic FAQs, while complex issues are flagged for human support.

• A social media agent may only monitor mentions or reply to comments without creating new posts.

IV. Create an Engaging Sales Research AI Agent with Leap AI

In this part, we’ll go through the process of building a sales research AI agent using Leap AI that can gather valuable information about sales leads from just an email address. This agent will help you quickly collect details like company information, job title, and even social media links, making your lead enrichment faster and more effective.

Step 1: Sign Up and Log In to Leap AI

• Open your browser and go to the official Leap AI website. If you don’t already have an account, locate the “Sign Up” button, in the top right corner. Once signed up, log in to access the dashboard where you’ll be able to create, manage, and test your AI agent workflows.

  

Step 2: Start a New Workflow

1. Navigate to the Workflows Tab:

   • On the left-hand side of the dashboard, look for a tab labeled "Workflows." This is where all workflow creation and management happen. Click on it to access the workflow builder.

     

2. Create a New Workflow:

   • In the workflow section, click the “Create Workflow” button on the screen. This opens a blank workflow where we’ll build your research agent step-by-step.

Step 3: Add and Set Up an Email Input Field

1. Add Input Step:

   • In the workflow builder, click on the “Input” step.

   • In the right-side panel, click the “Add Input” button to start setting up the email input field.

     

2. Configure the Input Field:

   • For Input Type, select "Short Text" and label it as "email."

   • Set a placeholder like "[email protected]" to show an example format.

   • Make this field required to ensure every run includes an email.

   • Set Variable Name to “email” so it’s easy to reference later in the workflow.

     

3. Save the Input:

   • Click “Add” to save this input field. Now, your AI agent is set to receive email addresses to begin lead research.

     

Step 4: Customize the Research Agent with a Prompt

1. Add a Research Step:

   • Below the Input step in the workflow builder, click on “Add Step” to bring in a research action.

     

   • In the pop-up menu, search for “Research” and select "Research Agent" as the action type.

     

2. Edit and Personalize the Prompt:

   • Click on the newly added Research Agent step. You’ll see a prompt area on the right panel with some default text.

   • Delete any default variables (e.g., 'firstname,' 'lastname') to customize the prompt specifically for lead research.

3. Use the Email Variable in the Prompt:

   • Click into the prompt text box. When a dropdown menu appears, select "Input" and then choose "email" to insert it as a variable.

4. Replace the prompt text with:

Enrich this lead by finding everything you can about this person: {{email}}

After gathering data reply with the most likely accurate information in the following format (fill as many as you can, or reply with N/A for each):
- Company Name
- Company Description
- Company Website
- Job Title/Role/Description
- Demographics: Age, Gender, Location, etc..
- Relevant Links (linkedin, twitter, etc)
- Citations/Sources (where you found this information, Title + URL)
- Anything else you think is relevant

Be very specific about what you are looking for. If you don't find anything, reply with N/A.

5. Save the Prompt:

   • After pasting in the prompt, make sure everything looks good and then save it. This prompt instructs the AI agent to dig deep for as much lead info as possible using the provided email.

     

Step 5: Format the Output for Easy Reading

1. Select Output Type as Markdown:

   • In the workflow builder, click on the “Output” step.

   • On the right-side panel, find the Output Type dropdown and select "Markdown." This makes the results easy to read and neatly organized.

     

2. Label the Output for Clarity:

   • Set the output label to something like “research” so it’s easy to identify in the results.

3. Add the Output Variable:

   • In the Markdown Value text box, click to open a dropdown. Select the output variable from the Research Agent step to link it to the results.

     

4. Save Output Settings:

   • Double-check your settings and save them. Now, the agent will format all lead information in a readable markdown format, making it easy to scan for details at a glance.

     

Step 6: Test and Launch Your Research AI Agent

1. Run Initial Tests:

   • Click on the “Test” tab in the workflow builder to see how your agent performs.

   • Enter a sample email (e.g., [email protected]) in the input field and click “Test.” The AI agent will run the workflow and display the output on the right panel.

2. Review the Output:

   • Look through the output to ensure it provides the details you expect. Check for accuracy and completeness to make sure the agent is working as intended.

3. Publish the Workflow:

   • Once satisfied with the test results, click the “Publish” button in the top-right corner. This makes your workflow live, so it’s ready to start helping you with real lead research.

     

4. Run Options: Dashboard or Bulk Processing: In the dashboard, click “Run In Dashboard” to see different run options:

   • Single Run: Enter an email and run the agent for a one-time lookup.

     

   • Bulk Run: Upload a CSV with multiple emails to run the workflow in bulk.

     

   • Scheduler: Set it to run automatically at set intervals for regular lead updates.

     

Choose the option that best suits your needs and watch your AI agent in action, gathering and organizing key info about your sales leads!

With this setup, your AI agent is now a powerful tool for enriching lead data. It takes an email, runs it through a detailed search, and returns a well-organized set of information, all ready for you to act on. This workflow allows you to handle lead research in bulk, on a schedule, or one at a time.

Helpful Tips

1. Explore Pre-Made Tools and Templates

• Where to Find: Head to the “Tools” section in Leap AI’s dashboard.

• Why It Helps: Leap AI offers pre-made templates that are designed to save you time. You can find templates that might already be close to what you need and then customize them to fit your specific requirements.

• How to Use: Start by exploring templates that are similar to sales research, like data gathering or lead enrichment workflows. These can give you a solid starting point, and often just a few tweaks will make them perfect for your project.

2. Manage Your Credits Wisely

• Why Credits Matter: Each workflow you run, including tests, uses up credits on Leap AI. Running out of credits can halt your workflows, so it’s important to keep an eye on your balance.

• How to Save Credits:

  • Test with Sample Data: Instead of running tests on real emails repeatedly, create a few sample entries to avoid overusing credits.

  • Optimize Your Workflow: After testing, try to refine your workflow so it’s efficient. Remove any unnecessary steps that might consume extra resources.

  • Plan for Bulk Runs: If you have a large list of leads, consider running them in batches to avoid spending too many credits all at once.

3. Monitor Performance Regularly

• Check Your Output: Periodically review the agent’s output to ensure it’s accurate and relevant. Sometimes, AI needs a bit of adjusting to give the best results.

• Adjust for Quality: If the agent returns irrelevant or sparse information, try tweaking the prompt with more specific instructions or different phrasing to guide the AI more accurately.

• Set Notifications: Many platforms allow you to set notifications if a workflow fails or runs into issues, so you can stay on top of any problems quickly.

Conclusion: My Personal View

I have to say, using Leap AI to build an AI agent like this is about as easy as it gets. From my perspective, the biggest pro here is the simplicity - no coding, no complex technical setup. Just plug in a few details, follow the workflow, and you’re set. It’s perfect if you’re new to AI and want something that works right away without a learning curve.

However, there are a few cons to keep in mind. Leap AI is excellent for basic functions and straightforward tasks, but as soon as you need something more complex - like detailed multi-step workflows or advanced customization - you might feel a bit limited. You’re also managing credits, so you’ll want to keep an eye on usage if you’re running larger operations.

Overall, it’s a great place to start. You get a fully functional AI agent with minimal effort, which is impressive. But if you’re ready to go a step further and create something with a bit more flexibility, stay tuned.

In the next lesson, I’ll guide you through a more advanced setup, don’t worry, it won’t be too tough, just a few extra steps to give your AI agent more power!