How do you create and integrate an AI agent?

Introduction

Artificial intelligence (AI) is transforming businesses at lightning speed. Among its most promising applications are AI agents stand out by revolutionizing the way organizations automate their processes, interact with their customers, and improve their operational efficiency.

Why are AI agents so popular?

Companies are constantly looking to optimize their operations, and AI agents offer a powerful and scalable solution. Here are just a few of the reasons for their mass adoption:

1. Continuous availability AI agents operate 24/7, responding to customer needs at any time.
2. Intelligent automation They perform repetitive tasks, such as processing requests or managing leads, freeing up time for human teams.
3. Improved precision Unlike humans, AI agents don't get tired, which reduces errors.

Why read this article?

If you are an SME or a growing business, you may be wondering: where to start creating and integrating an AI agent?
This article offers a clear methodology and practical advice for :

• Define the precise objectives of your AI agent.
• Select the right tools and technologies.
• Understand the crucial role of prompts in AI efficiency.

By following this step-by-step guide, you'll be better prepared to reap the benefits of an AI agent, while avoiding the common pitfalls associated with its development and integration.

Defining the objectives of an AI agent

The success of an AI agent depends on clear objectives and well-defined requirements. This initial step ensures that the solution really does meet the company's needs.

1. Needs analysis

Identify the tasks or processes to be automated :

• Activity mapping List recurring or time-consuming tasks.
• Friction points : Detect manual steps that slow down your operations.

Example An online store can automate FAQ responses and simplify return processes.

2. Set measurable objectives

Turn your needs into concrete objectives:

• Response time Reduce delays to less than 5 seconds.
• Productivity : Free up 50 % of support team time.
• Conversion : Increase sales by 20 % through AI recommendations.

The benefits of KPIs These indicators enable us to monitor performance, adjust strategies and demonstrate return on investment.

Choosing the right tools and technologies

Once the objectives have been defined, the choice of tools and technologies becomes a key step. The performance of an AI agent depends directly on the platforms used for its design, automation and integration.

Automation platforms for designing AI agents

Automation tools simplify the development of AI agents by offering preconfigured and customizable solutions. Here are the main platforms we use that you should consider:

Make.com

• Highlights Intuitive, powerful and flexible, Make.com lets you create complex workflows linking several systems (CRM, e-commerce, support).
• Use cases Automate lead management or send automatic alerts based on user actions.

Flowmattic

• Highlights : Specially designed for businesses that want to keep their data on their server, Flowmattic offers seamless integration with WordPress and WooCommerce.
• Use cases Manage specific actions on a site, such as sending e-mails after a purchase or synchronizing customer data.

n8n

A powerful self-hosted or cloud-based open-source solution, ideal for automating complex workflows without recurring costs.

• Benefits Highly customizable, with existing free templates, and suitable for both small and large structures.
• Use cases Automate internal processes, connect multiple tools with customized data flows.

GoHighLevel LeadConnector

• Highlights : Focused on lead management and marketing, this tool is ideal for automating campaigns and tracking performance in real time.
• Use cases Automate lead nurturing and conversion reporting.

2. Integration of RAG (Retrieval-Augmented Generation)

To guarantee precise, contextualized answers, integrate the RAG to your AI agent. This method combines :

• Text generation The AI agent uses models like GPT to formulate responses.
• Information retrieval It accesses databases or APIs to provide up-to-date data.

Benefits :

• Improved response accuracy.
• Ability to manage complex queries combining static and dynamic data.

Practical example

A customer asks, "What are the delivery times for my order?"

• AI Generation : Formulation of response based on demand.
• Recovery : Search the database to provide an exact date and tracking link.

3. Tool selection criteria

To choose the right tools, ask yourself the following questions:

1. Compatibility Does the tool integrate with your existing systems (CRM, ERP, e-commerce)?
2. Scalability Can it evolve with your future needs?
3. Ease of use Will your team be able to adopt it easily without complex training?
4. Budget Is it affordable, even for small businesses?

Prompting in AI agent design

Visit prompting is the art of formulating clear, detailed instructions to guide the behavior of an artificial intelligence (AI) model. When well designed, a prompt maximizes the accuracy, relevance and consistency of the responses generated by the AI.

A good prompt has several essential components: Role, Objective, Context, Tone and Style, Formatting guidelinesand Exclusions. Here's how to structure them effectively.

1. Define the Role

The role tells the AI what type of "personality" or specialist it should embody. This guides the style and knowledge mobilized by the model.

Clear example :

• Prompt You're a website optimization expert with 10 years' experience. Your objective is to provide concrete recommendations for improving the loading speed of an e-commerce site."

→ The role here is clearly defined ("optimization expert") and establishes AI's authority and perspective in its field.

2. Set a clear goal

The objective describes the purpose of the prompt, i.e. the expected result. This can include actions such as generating an analysis, providing advice or writing specific content.

Clear example :

• Prompt You're a UX design specialist. Your objective is to create a 5-point checklist to improve the usability of a mobile site aimed at young users (18-25)."

→ Here, the objective is defined: a concise 5-point checklist.

3. Provide relevant context

Context adds the details necessary for the AI to understand the nuances of the task. This can include information about the target audience, specific data, or constraints.

Clear example :

• Prompt : "As an AI project manager, you're advising a company launching a chatbot for a travel booking platform. Provide tailored recommendations to novice users visiting the site for the first time."

→ Context includes:

• The nature of the business (reservation platform).
• Target audience (novice users).
• The final objective (adapted advice).

4. Specify Tone and Style

Tone and style define how information is communicated. This can include a professional, friendly, persuasive or educational tone.

Clear example :

• Prompt As a technical consultant, write a clear, accessible answer for a non-technical customer who wants to understand why his site is slow. Use a reassuring and educational tone, without technical jargon."

→ This prompt ensures that the tone is adapted to the customer's level of understanding.

5. Add Formatting Guidelines

The expected format of the answer is essential for structuring the results. This may include lists, paragraphs, tables or multi-section answers.

Clear example :

• Prompt Provide a three-part answer:
  1. Possible causes of website slowness.
  2. Three concrete recommendations for solving these problems.
  3. Specific tools to measure site speed".

→ This format guides AI to structure a clear and comprehensive response.

6. Mention Exclusions

Exclusions specify what should not be included in the answer, to avoid unnecessary or irrelevant information.

Clear example :

• Prompt "Provide advice on how to improve an e-commerce site, but don't include recommendations related to purchasing third-party services or completely redesigning the site."

→ Exclusions prevent out-of-frame responses.

Example of a Complete and Structured Prompt

Here is an example incorporating all the components described:

Prompt :

• Role "You are an experienced SEO consultant."
• Objective "Your goal is to provide specific recommendations to improve a site's ranking in Google search results."
• Context The site belongs to an SME in the fashion industry, and targets mainly young adults (18-30) in France. Currently, the site suffers from slow loading times and poorly optimized pages."
• Tone and Style "Use a professional but approachable tone, suitable for business owners with little technical knowledge."
• Formatting guidelines Present your recommendations in the form of a numbered list. Add an explanatory sentence for each point."
• Exclusions "Never mention strategies that require a high advertising budget."

Summary

A well-designed prompt rests on six pillars: Role, Objective, Context, Tone and Style, Formatting guidelinesand Exclusions. By integrating these elements, you guide the AI to produce precise answers, aligned with your needs and adapted to the target audience. This structure maximizes efficiency and reduces the risk of vague or irrelevant answers.

How to create an AI agent

Having understood the importance of prompting, it's time to dive into the process of creating the AI agent itself. This stage combines data collection, model selection, the integration of techniques like RAG (Retrieval-Augmented Generation), and the use of optimized prompts. Here's a detailed guide to the necessary steps.

1. Data collection and preparation

Data is the fuel of a high-performance AI agent. Its quality and relevance will determine the accuracy of the answers generated.

Key steps for data collection :

1. Identify relevant sources :
   • Internal databases (CRM, ERP, existing FAQs).
   • External data such as customer feedback or market trends.
2. Structuring data Organize information in easily accessible formats (JSON, CSV, etc.).
3. Data cleansing Eliminate duplicates, inconsistencies and obsolete information to ensure reliable answers.

Example :

For an e-commerce chatbot, collect data such as :

• The most frequently asked questions.
• Average delivery times by region.
• Return and refund policies.

2. AI agent development

When creating an artificial intelligence (AI) agent, the choice of model is a crucial step that directly influences its performance and its suitability for your specific needs. Here's a comparison of the main AI models available, including DeepSeek V3, to help you with this decision:

IA model	Highlights	Ideal use case
OpenAI GPT-4	- Smooth, context-sensitive text generation. - Versatile for a variety of uses. - Broad community support.	Customer support, advanced chatbots, content creation.
Anthropic Claude 3.5 Sonnet	- In-depth contextual understanding. - Designed to avoid harmful or biased responses. - Handle large volumes of data with extended context.	Applications requiring high reliability and extensive context management.
DeepSeek V3	- Open source model with 671 billion parameters. - Efficiency in reasoning, coding and mathematical calculation. - Access free of charge and without registration.	Tasks requiring complex reasoning, open source development, barrier-free accessibility.
Google Gemini	- Multimodal model integrating text and image. - Tight integration with Google services. - Regular updates to improve performance.	Applications requiring multimodal understanding, integration with the Google ecosystem.

Factors to consider when making your choice :

1. Use cases Identify whether your project requires fluent text generation, multimodal comprehension, or processing of large volumes of contextual data.
2. Data sensitivity For areas where accuracy and ethics are paramount, models like Anthropic Claude 3.5 Sonnet, designed to minimize bias, may be preferable.
3. Budget and accessibility If you're looking for a free, open-source solution, DeepSeek V3 offers a robust alternative with no registration fees.
4. Technology integration Make sure the model you choose integrates well with your existing systems and meets the technical requirements of your project.

Practical example :

• For a chatbot designed for customer support requiring precise answers and extensive contextual understanding, OpenAI GPT-4 would be suitable.
• If your project involves complex data analysis with an open source solution, DeepSeek V3 would be a wise choice.

By carefully evaluating these factors, you'll be able to select the AI model best aligned with your specific objectives and constraints.

Integrating AI agents into existing systems

For the AI agent to work effectively, it must be seamlessly integrated with the systems already in place within the company. Here are the key steps to successful integration:

Compatibility and interoperability

The first step is to ensure that the AI agent is compatible with your existing tools, such as CRM, ERP or LMS systems. Thanks to APIs (programming interfaces), the agent can connect directly to these systems, enabling fluid communication and real-time data synchronization.

Workflow automation

Once the AI agent has been integrated, its role goes beyond the simple execution of isolated tasks. Its functionality is to orchestrate complex workflows via automation platforms such as Make.com or Flowmattic.

• Example 1: When a customer submits a request via the chatbot, the AI agent can automatically create a ticket, notify the relevant team and send a confirmation to the customer.
• Example 2: With GoHighLevelthe AI agent can manage a complete lead generation cycle: capturing information, tracking prospect status, and triggering personalized emails.

Personalization and context

To guarantee an optimal user experience, the AI agent must be configured to take into account the specificities of your business. For example, it can adapt its responses according to customer profile or business context.

This requires the integration of a knowledge base. An approach such as RAG (Retrieval-Augmented Generation) can also draw on existing databases to provide precise, contextual answers, reinforcing its relevance.

Testing, validation and optimization of prompts

Once the AI agent has been integrated, it is crucial to ensure that it tests functionality and that it functions correctly and meets expectations. This step involves a series of thorough tests, rigorous validation and continuous optimization of prompts to ensure optimal performance.

Test phase

• Real-life scenarios : Test the AI agent in situations representative of user interactions, such as customer support requests, technical questions or automated tasks. Ask the same questions as users, and let it answer questions.
• Performance: Measure key indicators such as response accuracy, response time and workflow fluidity.
• Error analysis : Identify areas where the AI agent fails to understand or provide appropriate responses.

Validation with end users

• Collecting feedback : Solicit end-users to evaluate their experience with the AI agent.
• Prompt review : Adapt the agent's instructions and scenarios according to the feedback, to fine-tune his responses.

Prompt optimization

• Role of prompts : Prompts are essential to guide the AI agent in its interactions. They must be clear, specific and adapted to needs.
• Example: Rather than asking "What can I do for you?", an optimized prompt might be "Would you like information about your order, a refund, or anything else?"
• A/B tests : Experiment with different prompt formats to determine which offer the best performance.

Continuous updating

• Adapting to change : Business needs are changing. So it's important to regularly revise prompts and workflows to reflect these changes.
• Performance monitoring : Set up metrics to monitor AI agent effectiveness, such as request resolution rates or user satisfaction.

Deployment and maintenance

Once testing is complete and the agent has been validated, it's time to deploy it in your operational environment. However, maintenance is essential to ensure long-term success.

Production start-up

• Progressive deployment : Introduce the AI agent in stages, starting with simple use cases before covering more complex scenarios.
• Team training : Provide your employees with hands-on training so they understand how to use the agent and solve common problems.

Continuous monitoring

• Performance: Regularly monitor key metrics (response time, customer satisfaction rate).
• Regular revisions : Update the AI agent to reflect changes in your systems or user expectations.

Proactive maintenance

• Technical checks : Make sure the agent is compatible with new versions of connected tools.

Conclusion

Deploying and maintaining an AI agent is not just a technology project, but a real business transformation lever. Thanks to rigorous testing, well-optimized prompts and ongoing maintenance, your AI agent can not only automate processes, but also significantly improve the user experience.