Generative AI and AI Agents: strengths, critical issues and integration methods
AI Agent and Generative AI: definitions and use cases
AI Agents are programs designed to receive input, analyze problems, and act autonomously to complete specific tasks. They use a Large Language Model (LLM) as a reasoning engine, external tools and APIs to interact with the real world and memory systems to maintain context and the state of activities. Their distinctive element is their ability to make independent decisions and act proactively.
Generative AI, on the other hand, is specialized in creating new content-such as text, images, videos, or code-based on LLMs and deep learning models trained on large amounts of data. It operates in a reactive mode, generating output from prompts provided by the user, without taking autonomous initiatives.
Main differences between AI Agents and Generative AI
The fundamental difference lies in function and degree of autonomy. Generative AI is reactive: it waits for an input and produces coherent and creative content, without undertaking autonomous actions.
AI Agents, on the other hand, can analyze situations, make decisions and act independently to achieve specific goals. While a generative system can provide a list of options or content, an AI agent can autonomously carry out the necessary actions to complete the task.
Limitations of Generative AI
Although Generative AI is extremely effective at producing text, images, videos, or code, it has limitations when a concrete interaction with the real world or access to up-to-date data is required. The model relies on training information with a cutoff date, which means it cannot know events or data occurring after that point.
Moreover, Generative AI does not truly “understand” what it creates: it merely calculates the most likely output based on learned patterns, without awareness or critical judgment. This approach makes the system static within defined boundaries, incapable of undertaking autonomous actions or handling complex tasks that require multi-step planning.
Integration of Generative AI into AI Agents
AI Agents stand out for their ability to maintain a memory that goes beyond the simple context window of generative models, allowing them to preserve the state of tasks, the history of interactions and user preferences across multiple steps. Thanks to continuous learning, the agent can constantly refine its decision-making, adapting actions and strategies in real time based on new information.
In this context, Generative AI also becomes a tool: the Large Language Model acts as the agent’s reasoning engine, processing data and suggesting solutions, while the agent’s infrastructure coordinates external tools and concrete actions. For example, an AI Agent can integrate Generative AI to automatically draft a personalized email as part of a complex workflow.
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