How to develop AI agents: A guide for business leaders

AI agents have become a key tool for companies looking to optimize processes, improve customer service, and make data-driven decisions. But what exactly is an AI agent, and how can you develop one that aligns with your company's goals?

We explain it step by step in this article.

What are AI agents?

An AI agent (or intelligent agent) is a system that can perceive its environment, make decisions autonomously, and execute actions to achieve a specific goal.

These agents can range from a chatbot that answers frequently asked questions to an advanced system that optimizes logistics operations in real time.

Unlike traditional rules-based automation, AI agents incorporate artificial intelligence techniques such as machine learning, natural language processing (NLP), and symbolic reasoning, allowing them to adapt, learn, and evolve over time.

“An agent takes the power of generative AI a step further, as rather than simply assisting you, it can work with you or even on your behalf. Agents can perform a variety of tasks, from answering questions to more complex or multi-step tasks. What differentiates them from a personal assistant is that they can be customized to specialize in a specific area,” Microsoft explained.

Business Benefits of AI Agents

Implementing AI agents in your company can be a game changer. Some of the most significant benefits are:

• Automation of repetitive tasks, freeing up time for strategic activities.
• 24/7 customer service, improving the user experience.
• Real-time analysis and decision-making from large volumes of data.
• Reduction of human errors in critical processes.
• Operational scalability, without proportionally increasing costs.

For companies looking to stay competitive, AI agents represent an opportunity for operational and cultural transformation.

“The launch of an AI agent typically begins with rapid results, automating repetitive queries and reaching a 20% to 30% automation rate within a few weeks. However, scaling beyond this point to meet complex business needs requires consistent resource allocation and well-defined processes,” they highlighted in a Forbes article on the topic.

Key steps for developing AI agents

1. Define the business problem

Before writing a single line of code, it's essential to clearly identify what problem you want to solve. Is it reducing customer response times? Optimizing inventory? Improving demand forecasting?

This step helps define the scope of the AI ​​agent and align its capabilities with business objectives.

Along with the problem, you must identify the objective of this agent: "Find out what the organization needs to achieve with business model innovation," stated a report by Gartner.

2. Choose the right type of agent

There are different types of AI agents, each with specific characteristics. Some examples are:

• Reactive agents: respond directlyto environmental stimuli with no memory of the past. Useful for simple, repetitive tasks.
• Model-based agents: They build an internal representation of the environment to anticipate consequences. Ideal for dynamic environments.
• Learning agents: They incorporate machine learning algorithms to improve their performance over time.
• Collaborative agents: They interact with other agents or humans to achieve joint goals.

Choosing the right type depends on the complexity of the environment and the requirements for autonomy, adaptability, and interaction.

3. Collect and Prepare Data

AI agents learn and make decisions based on data. Reliable, representative, and relevant data sources are essential. Furthermore, this data must be cleaned, labeled, and normalized for effective use in AI models.

At this stage, many companies face challenges such as information silos, poor data quality, or a lack of structure. Overcoming these challenges is key to the project's success.

To effectively train AI agents, Salesforce stated that it uses real-world, representative data such as conversation transcripts (chats, emails, or tickets), voice recordings (to understand accents and intonations), and records of past interactions (to identify patterns and common needs). The quality of this data is key to achieving an accurate and helpful agent.

4. Choosing the technology and development tools

Today, there are multiple frameworks, libraries, and platforms for developing AI agents. Some of the most popular are:

• TensorFlow or PyTorch for deep learning.
• spaCy, NLTK, or OpenAI for natural language processing.
• Reinforcement Learning libraries such as Ray RLlib or Stable Baselines.
• Platforms such as Dialogflow, Rasa, or Microsoft Bot Framework for conversational agents.

Having a team with experience in these technologies is crucial for a successful implementation.

5. Design the agent's architecture

This defines how the agent will interact with its environment. For example:

• What sensors or data sources will it use?
• How will it make decisions? With rules, predictive models, or reinforcement learning?
• How will it act? Will it send alerts, perform automatic actions, or suggest decisions?

A well-designed architecture facilitates scalability, maintenance, and integration of the agent into existing business systems.

6. Training and Testing

If the agent includes machine learning components, it must be trained with historical data. This process should include:

• Cross-validation of the model.
• Evaluation of metrics such as precision, recall, or F1 score.
• Testing in controlled environments (sandbox) before moving to production.

In addition, it is vital to ensure that the agent is not only accurate, but also interpretable, ethical, and secure.

7. Deployment, Monitoring, and Continuous Improvement

Once deployed, the agent must be constantly monitored to evaluate its performance, identify errors, and detect opportunities for improvement. User feedback and analysis of the generated data are valuable sources for iterating and evolving the agent.

Remember that AI agents are not static products, but living systems that must grow with the organization.

Common Business Use Cases

AI agents can be applied in almost any industry. Some common use cases include:

• Retail: virtual shopping assistants, demand prediction.
• Banking and Finance: credit risk analysis, fraud detection.
• Healthcare: automated triage, patient tracking.
• Logistics: route optimization, inventory management.
• Human Resources: automated profile selection, digital onboarding.

Each case represents an opportunity for cost savings, efficiency improvements, and competitive differentiation.

The development of AI agents is one of the most promising technological ventures for modern companies. But for its successful implementation, having good algorithms is not enough. It requires a well-defined strategy, solid technical execution, and a strong commitment to ethics and data quality.

If you are considering incorporating AI agents into your company, now is the ideal time to start. Whether pilot projects or larger-scale initiatives, having the support of experts in software development and artificial intelligence will make all the difference.

Do you want to explore how AI agents can transform your business? At Rootstack, we help companies across all industries design and implement customized intelligent solutions. Contact us!