AI agents: the future of AI beyond large language models

In recent years artificial intelligence has made huge strides, thanks above all to large language models (LLMs), models that can generate text, answer questions and even write code. However, traditional LLMs have significant limitations that prevent them from being used in more complex scenarios. The natural evolution of these models is AI agents: systems that can make decisions on their own, interact with external tools and adapt to the goals they are given. Let’s look at what they actually are and what sets them apart.

What an LLM is and how it works

An LLM is a machine learning model trained on huge amounts of text data. Among the best known are GPT-4 (OpenAI), Claude (Anthropic), Gemini (Google DeepMind) and Mistral.

LLMs can generate text in an extremely sophisticated way, but they have no real understanding of the world. For all their power, they suffer from some fundamental limitations that make them poorly suited to more advanced scenarios:

1. No long-term memory, because each request is processed independently of the previous ones, which makes it hard to keep the context in complex conversations.
2. No decision-making autonomy: an LLM responds to prompts, but it cannot take the initiative or act on its own without continuous input from the user.
3. Inability to interact with external tools: without specific integrations, an LLM cannot browse the internet, run code or access databases.
4. Difficulty with multi-step planning: when a task requires several steps (such as booking a flight, analysing a report and writing a summary), an LLM struggles to manage the process on its own.

These limitations have pushed research towards a new generation of systems: AI agents.

What are AI agents?

An AI agent is a system based on an LLM but equipped with additional capabilities that allow it to act autonomously to reach a goal. Its main features include:

1. Memory and context that persist over time
2. Interaction with external tools, through integrations with APIs, browsers, databases and software
3. Multi-step planning and reasoning: agents can break complex tasks down into sub-tasks and complete them without continuous supervision
4. The ability to learn and improve, through feedback and new information.

This shift from LLMs to AI agents represents a paradigm change, taking AI from a simple text-generation tool to an entity that can make decisions and interact with the world in a more sophisticated way.

The architecture of AI agents: beyond text generation

How does an AI agent work? Which technologies make it possible?

One of the most powerful libraries for building AI agents is LangChain, an open-source framework for building advanced LLM-based systems that integrate external tools, memory and multi-step workflows.

The core components

An AI agent is not just a language model, but a complex system. The main elements of an agent built with LangChain are:

1. LLM: the heart of the agent, responsible for processing natural language
2. Prompt engineering: the technique of guiding the model’s behaviour through structured prompts
3. Memory: lets the agent remember information between interactions (for example in long conversations)
4. Tools or plugins: external APIs, databases, internet search functions, calculation engines and so on
5. Planning and decision-making: algorithms that help the agent break complex problems down into manageable sub-tasks.

LangChain provides a modular framework that makes it simple and effective to integrate these elements, and makes it possible to build AI agents that are powerful, flexible and interactive. The main challenge, however, is managing the agent’s decision-making: which tools should it use? When should it search? When should it generate text?

Practical applications of AI agents

The growing adoption of AI agents by companies is transforming several industries, allowing them to automate complex tasks, optimise decision-making and improve operational efficiency.

AI agents in customer support: smart chatbots and advanced assistance

One of the fields that adopted AI agents fastest is customer support (for example in e-commerce). Chatbots used to be simple systems based on static rules; today, thanks to AI agents, it is possible to offer advanced, personalised and context-aware conversational support.

An AI agent can handle customer requests autonomously, answering questions about products, suggesting items based on past preferences and even processing refunds or complaints.

Real-world example: companies such as Amazon and Shopify use AI agents to make customer support more efficient, cutting waiting times and providing more accurate answers.

Business automation: AI agents for productivity

In this scenario, the revolution these agents can bring to internal automation in companies should not be overlooked: they can handle repetitive processes (such as reports) and improve productivity (discover Neurally, our assistant). They can also collect data from different company sources (CRM, ERP, internal databases), process it and generate detailed reports without human intervention.

Real-world example: companies such as Microsoft and IBM are developing agents that can produce meeting summaries, automatic emails and analyses of financial data.

AI agents in finance: market analysis and decision-making

In finance, the ability to analyse large amounts of data quickly and make informed decisions is essential. Here, AI agents can be used to:

• Analyse market sentiment based on news and social media
• Forecast share price trends using historical data and current trends
• Manage investment portfolios automatically

Specifically, an AI agent can monitor financial markets in real time, spot patterns and send buy/sell signals based on predefined parameters. Trading platforms such as Robinhood and eToro are experimenting with AI agents to give investors personalised suggestions.

Today we see AI agents in customer support, finance, scientific research and even robotics, but their potential is still largely unexplored. What will happen when AI agents can make high-level decisions with complete autonomy? We could see the birth of companies run entirely by AI agents, new forms of automation never seen before, or a further merging of the digital and physical worlds.

While the future looks exciting, it also raises crucial challenges: ethics, security, governance and the very role of human beings in a world increasingly driven by artificial intelligence. Will we manage to strike the right balance between AI autonomy and human control?