AI Agents
An Artificial Intelligence Agent is an autonomous system that carries out tasks by designing workflows with the tools it has available.
These agents perform functions that go far beyond Natural Language Processing, meaning they don't just understand and respond, they make their own decisions, solve problems, interact with external systems and execute actions.
They solve complex tasks within a company, like software design, IT department automation, code generation and conversational assistance. They use advanced processing techniques from Large Language Models (LLM) to understand and respond to the user and identify when to call on external tools.
How AI Agents Work
At their core, agents rely on these LLM. That's why they're often referred to directly as LLM. Traditional models produce responses based on the data they were trained on, so they're limited to that knowledge. Agentic AI, on the other hand, has the ability to call other tools from its backend to get up-to-date data, optimize workflows and create subtasks autonomously to solve complex tasks, when it can't find the data it needs within its model.
In this process, the autonomous agent learns to adjust to what the user expects over time. By being able to store past interactions in memory and plan future steps, it offers a more personalized experience and more complete responses. Moreover, the ability to call tools without human intervention expands the range of possible uses in real-world scenarios.
Although agents make decisions on their own, they need humans to define goals and rules for them. This includes 1) the team that builds them, 2) the team that deploys them, and 3) the user who defines what needs to be achieved. Given these instructions, the agent breaks down tasks to improve its performance and, in essence, builds its own plan of tasks and subtasks to reach a complex goal.
For simple tasks, the agent doesn't need a prior plan. It responds to what's requested, evaluates whether it meets the goal through feedback and, if not, adjusts its response. For complex tasks, on the other hand, it builds a plan in advance with subtasks and evaluates results based on rules or defined data. In both cases, the agent adjusts its behavior until it gets closer to the goal.
Reasoning With Available Tools
Agents base their actions on the information they have. However, they often don't have all the information needed to solve complex tasks. To close this gap, they turn to external databases, web searches, API calls and even other agents.
Once they gather the information they need, they can begin the Agentic Reasoning process. This process involves continuously validating the action plan and autonomous decision-making, which results in a constant ability to adapt.
To illustrate this process, imagine a user who asks the agent to identify which would be the best week of the year to surf in Mar del Plata. Since the agent doesn't specialize in weather patterns, it needs to turn to external sources to get historical weather reports for the area from recent years. Even so, this information isn't enough, and the agent has to create a new subtask to consult a source specialized in surfing, in order to learn that high tides, good weather and little or no rain represent the best surfing conditions.
Combining all this information, the agent can predict which week of the year might have high tides, sunny days and a low chance of rain.
This continuous exchange of information between different tools is what makes Agentic AI more efficient and versatile than Traditional AI.
Learning and Reflection
AI agents improve their performance through feedback mechanisms, which can come from human users (human-in-the-loop, HITL) as well as from other agents. Once the agent generates a response, it can compare it against the expected goal and use that feedback to adjust its behavior in future interactions. This way, it doesn't learn specific cases, but patterns for how to better solve different types of problems.
In Multi-Agent Systems, feedback between different agents helps reduce human intervention, since the agents themselves can evaluate and correct each other. This continuous improvement process is known as Iterative Refinement and involves progressively adjusting how the agent reasons and acts based on the results obtained.
To avoid repeating mistakes, agents can store information in a database about how they solved past obstacles, letting them optimize decision-making and gradually adapt to new user goals and preferences.