How to create AI agents today?
The Italian and international technology landscape has reached a turning point. While 2024 and 2025 saw the explosion of generative AI, 2026 is officially the year of autonomous AI agents. In Italy, the artificial intelligence market has seen record growth of 58%, reaching a value of €1.2 billion. Today, 88% of organizations globally have already integrated AI into at least one business function, and over half of companies (52%) actively use fleets of AI agents in production.
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Table of contents
What are AI Agents?
To understand how to create AI agents, we must first distinguish between a simple chatbot and an autonomous agent. While traditional chatbots follow rigid sequences, today’s AI agents are “digital employees” capable of planning, reasoning, and acting autonomously to achieve a complex goal.
An AI agent is an artificial intelligence system designed to operate autonomously toward a specific goal: it doesn’t simply generate responses, but observes the context, makes decisions, and takes concrete actions. Unlike a simple chatbot, an AI agent can plan multiple steps, use external tools (such as APIs, databases, or web services), and adapt its behavior based on the results obtained. This makes it particularly useful in complex scenarios, such as business process automation, continuous data analysis, or intelligent workflow orchestration. In short, an AI agent is an “intelligent executor” that transforms artificial intelligence from passive support to a true operational engine.
Thanks to the emergence of multi-agent systems and ever-deeper platform integrations, agents are now able to coordinate complex workflows that connect across sales, support, supply chain, and finance departments. This is enabled by the maturation of low-code platforms and robust accountable governance models, which make agent deployment not only faster but also more secure and easily scalable across the entire enterprise ecosystem.
How to create AI Agents: Business Impact and ROI
How to create AI agents? This is not just a technological choice, but a financial one. 74% of executives reported a return on investment (ROI) within the first year of deployment. Gartner predicts that by the end of 2026, 40% of enterprise applications will include task-specific AI agents.
High-performance organizations dedicate 70% of their efforts to process and people management, ensuring agents are integrated into workflows where they can generate a real impact on the bottom line.
2026 will see the definitive consolidation of AI agents as true autonomous digital coworkers, moving beyond the stage of simple task automation; it is estimated that 80% of enterprise applications will integrate native agents by the end of the year. The rapidity of this adoption is driving industry growth with an estimated CAGR of over 46%, generating massive increases in productivity and a significant reduction in operating costs.
In 2026, the question is no longer “how to create AI agents,” but “how to achieve value.” High-performance organizations invest over 20% of their digital budgets in data infrastructure and AI talent. The return on investment (ROI) is now measurable in terms of:
- Productivity
- Operating Costs
- Development Efficiency
How to create AI Agents: What technologies?
Answering the question “how to create AI agents” requires a combination of different technologies that enable the system to perceive, reason, and act autonomously. Among the key technologies are advanced language models (LLM), which enable understanding and generating natural text; machine learning systems, useful for analyzing data and improving decisions over time; integration with APIs and software platforms, which allows agents to interact with external services and automate complex workflows; and vector databases or knowledge bases, essential for storing structured and unstructured information and facilitating the rapid retrieval of relevant data. These technologies also include tools for coordinating multiple agents and collaborating with human users, enabling the management of complex and multidisciplinary scenarios, such as those found in healthcare, finance, or customer care. Thanks to this combination of technologies, AI agents become flexible and powerful tools, capable of acting as true strategic partners in organizations.
No-code platforms represent a major advantage for creating AI agents because they allow the development of intelligent systems without writing a single line of code. This means that even those without advanced programming skills can design, configure, and launch a customized AI agent.
The main advantages are:
- Speed of development: Creating and testing an agent takes hours or days instead of weeks or months.
- Accessibility: Marketing, customer care, or operations teams can build autonomous AI solutions without relying on developers.
- Flexibility and customization: No-code platforms offer drag-and-drop interfaces, integration with APIs and enterprise tools, and the ability to model customized workflows.
- Cost reduction: Less custom development means lower investment and faster payback times.
- Rapid iteration: Modifying and optimizing an AI agent is simple and straightforward, allowing you to quickly adapt to business changes or new customer needs.
In short, no-code platforms transform AI agent creation from a complex technical task into a rapid, accessible, and strategic process, making AI an operational tool accessible to all business teams.
Roadmap: How to create Ai Agents in 5 steps
The secret to success lies not only in the algorithm, but in the process. The best-performing companies follow the “10-20-70” principle: 10% effort on the model, 20% on data infrastructure, and 70% on people and processes.
- Goal definition
The first step to get to know how to create AI Agents is to identify a high-volume decision-making use case with clear criteria. Common examples include 24/7 customer support, lead nurturing automation, or HR process management.
- Platform Choice
Today, you no longer need to be a Python developer to build intelligent infrastructure. SaaS platforms like Crafter.ai allow you to create complex AI agents without writing code, using visual drag-and-drop interfaces. This reduces deployment times from months to a few weeks or even days.
- Configuring the Knowledge Base
An agent is only useful if they know your data. Using tools like Document Manager, you can upload PDFs, technical manuals, or product catalogs. The RAG system ensures that the agent only draws from certified sources, eliminating the risk of hallucinations.
- Designing behavior and tone
With the advent of GPT-5.2, the new professional standard that replaced legacy models like GPT-4o, you can customize not only what the agent says, but how they say it. You can adjust parameters such as warmth, enthusiasm, and tone of voice (e.g., friendly or formal) directly from the platform settings.
- Integration and Human in the Loop
The AI agent must live where your customers are: on your website, WhatsApp, Facebook Messenger, or internal systems like Salesforce and HubSpot. It’s essential to include a Human-in-the-Loop dashboard, which allows human agents to intervene in the most sensitive or complex conversations.
How to create AI Agents: Use cases
AI agents are finding application across a wide range of industries and are not limited to a single field or use case. They are transforming business processes, financial services, healthcare, and even everyday tasks such as planning or data analysis. In healthcare, for example, AI agents help patients manage their health more consciously, helping them understand the relationships between lifestyle choices, medication regimens, and risk factors. By automating complex tasks and easily integrating with existing software platforms and business tools, AI agents are becoming key elements for organizations aiming for growth and innovation. In multi-agent systems, multiple agents operate in parallel, simulating human-like social behaviors and developing complex interactions through autonomous, collaborative, and coordinated actions. In these contexts, AI agents can also work alongside human operators, who play a key role in ensuring effective communication and coordination, especially in multidisciplinary environments such as healthcare, where human expertise complements and enriches automation.
Customer Care
An AI agent can manage tickets, answer frequently asked questions, access the CRM to modify reservations or update information, offering personalized service 24/7.
HR and Recruiting
They can support candidate pre-screening, answer questions about company benefits, schedule interviews, and gather feedback on the company climate.
E-Commerce and Retail
Intelligent AI agents guide users through the purchasing journey, recommend products based on behavioral data, and manage returns or order tracking in real time.
Operations and IT
Agents can monitor systems, resolve common issues, open technical tickets, or initiate automated procedures (e.g., backup, reboot, log cleanup).
Marketing and Sales
They generate SEO content, suggest campaigns based on CRM data, automate lead response, and qualify incoming contacts.
Healthcare and Finance
They support document management, symptom triage, personalized advice on insurance products, and regulatory compliance.
Conclusions: Creating AI Agents is a real competitive advantage today
It’s no longer science fiction or prototypes in the testing phase. AI agents are now a mature, powerful reality, accessible to all Building AI agents means going beyond traditional automation and equipping organizations with systems capable of acting, adapting, and making decisions autonomously. AI agents reduce operational complexity, accelerate processes, and improve decision quality, freeing people and teams from repetitive tasks to focus on what generates real value. Thanks to their ability to collaborate with other agents and humans, AI agents become a multiplier of efficiency and innovation, transforming artificial intelligence from a simple technological support to a true strategic partner for business growth.
Creating AI agents means:
- Automating repetitive tasks
- Offering personalized experiences
- Reducing costs and operating times
- Scaling your business without scaling costs
Whether you want to improve customer service, enhance marketing, or streamline internal processes, now is the right time to get started.
Faq: How to create AI Agents
The cost of developing an AI agent varies based on the complexity of the project and the technologies used. A simple agent, built on no-code platforms, can be rapidly implemented for a few hundred or thousand euros per year, while a complex AI agent, integrated into corporate systems and trained on specific data, can cost tens of thousands of euros, including development, integration, and ongoing maintenance. In any case, more than the “pure” price, the true value lies in the agent’s ability to automate processes, support strategic decisions, and free up valuable team time, transforming it into a concrete investment in innovation and business growth.
The time required depends on the agent type and level of complexity. With no-code platforms, a basic agent can be created in a few hours or days, while a complex AI agent, integrated with multiple business systems and trained on specific data, can take weeks or months. The key is to plan the objectives and features carefully to optimize time and resources.
Not necessarily. Thanks to no-code platforms, even those with no programming skills can design, configure, and launch a custom AI agent. However, for more complex agents that require advanced integrations, custom machine learning, or sensitive data management, the support of AI experts and developers can ensure more effective and secure results.
