AI agents: architectures, operational capabilities, and impact on modern intelligent systems

A contemporary AI agent is typically composed of multiple functional modules. Generally, a large language model (LLM) serves as the decision-making engine. It does not merely generate text, but:

• Interprets complex goals;
• Breaks down the task into multiple stages;
• Evaluates alternatives and selects which one to execute.

Planning is what distinguishes an agent from a traditional chatbot. It includes methodologies for:

• Task decomposition: breaking down the problem into sub-tasks;
• Chain-of-thought / tree-of-thought reasoning: deciding to execute actions, observing results, and planning subsequent actions in the form of action chains or trees;
• Iterative refinement strategies: refining and perfecting future actions by observing the results obtained.

Unlike a chatbot, an AI agent can interact directly with external environments using software "tools" in the digital world via APIs, or even in the physical world via actuators or tools.

For example, an agent can:

• Use search engines and interpret their results;
• Write and execute code;
• Access internal or public databases;
• Interface with a PC via the operating system or specific software applications.

This transforms the LLM into an action orchestrator rather than just a text generator, capable of performing actions and modifying the environment in which it operates.

At each step, the agent constructs a "prompt" that includes the task to be performed, past observations, results produced by tools, etc., which is then processed by the LLM to define the next stage of planning.

The prompt thus accumulates all actions, observations, and results used to decide subsequent steps. Memory therefore enables persistence and continuity, and is divided into:

• Short-term memory: session context provided directly to the LLM;
• Long-term memory: stored and retrievable information accessed by the agent via semantic similarity.

The controller manages the agent's operational cycle through the loop: 

                                       observation → planning → action → feedback → status update:

• Verifies whether the goal has been achieved;
• Decides whether to continue, correct course, or terminate;
• Integrates external feedback.

A standard LLM receives an input (a prompt) and produces an output (text, an image, a table, etc.). The iteration is simply:

                                                                                       input → output 

An AI agent, on the other hand, iterates in a loop where it plans what to do, executes actions via tools, observes the result, and verifies how to proceed until the set goal is achieved. The iteration is thus:

                loop (input → planning→ multiple actions → tool interaction→ verification ) → final output 

This distinction is fundamental: the agent is a dynamic system, not an atomic generator.

Modern AI agents demonstrate capabilities that are not explicitly programmed:

• Limited operational autonomy (multi-step task execution);
• Self-reflection (evaluation of their own outputs);
• Iterative error correction;
• Context-based adaptive reasoning;
• Tool chaining (sequential use of different tools).

These properties emerge from the combination of model and architecture, not from the model alone.

Despite progress, AI agents exhibit structural vulnerabilities:

• Persistent hallucinations (plausible but false errors);
• Over-reliance on external tools;
• Difficulty in formal verification of results;
• Instability in long decision chains;
• Security issues (prompt injection, tool hijacking).

For these reasons, it remains essential to verify the results produced by an agent.

Per questo, è sempre fondamentale verificare il risultato prodotto da un agente. 

AI agents are transforming the software development paradigm:

• From deterministic applications to probabilistic and adaptive systems;
• From static workflows to dynamic pipelines;
• From rigid automation to controlled autonomy.

In an engineering context, this opens the door to:

• Autonomous debugging systems;
• Automated code generation and optimization;
• Intelligent microservice orchestration.

AI agents represent an architectural change rather than an incremental one: they do not merely improve model intelligence, but introduce a new level of abstraction between goal and action.

Current research directions point toward systems that are increasingly:

• Modular;
• Verifiable;
• Secure;
• Capable of operating in complex environments.

In this sense, AI agents are not simply advanced tools, but emerging computational cognitive systems set to redefine the very concept of intelligent automation.