The Anatomy of AI Agents: How Machines Sense, Think, and Act

AI Agents are becoming ubiquitous. From intelligent assistants on our devices to autonomous research tools and self-driving cars. According to 2025 data, 68% of SaaS companies already offer some type of AI agent in their products.

But what really powers these technologies? To use agents strategically, we need to understand their anatomy.

The Three-Stage Model

Every AI agent fundamentally operates in three stages that allow the machine to transform raw data into intelligent actions: Sensing (Perception), Thinking (Reasoning), and Acting (Action).

This model is the foundation that allows agents to perceive the world, make decisions, and interact with it effectively.

First Stage: Sensing (Perception)

Just as our eyes and ears capture light and sound, Sensing is the agent’s window to the world. This is where the agent collects the raw data needed to begin its decision-making process.

Input sources include:

Text: User inputs via chatbot, enabled by NLP (Natural Language Processing).

Sensors: Cameras, microphones, and sensors that capture the physical environment.

APIs and Events: Triggers from other systems, integrations, and cross-platform communication.

The diversity of collected information requires a sophisticated processing core to make sense of it all. That’s where the next stage comes in.

Second Stage: Thinking (Reasoning)

Once collected, information is sent to the Thinking stage — the brain of the operation. Here, raw data is analyzed, contextualized, and transformed into a coherent action plan.

To function, reasoning depends on essential context:

Knowledge Base: The agent’s long-term memory. Stores facts, rules, and context. Can use RAG (Retrieval-Augmented Generation) to query external sources.

Policy Information: Guidelines that orient decisions — goals, objectives, and priorities.

Without these context sources, the reasoning process would be like an engine without fuel or a map: powerful, but unable to go anywhere useful.

The reasoning process involves conditional logic, action planning, and decomposition of complex tasks into smaller, manageable steps.

Enabling technologies include Machine Learning — where the system continuously learns through reinforcement and pattern recognition — and Large Language Models (LLMs), fundamental for processing text and enabling complex reasoning like Chain of Thought.

Third Stage: Acting (Action)

After the reasoning process, the agent enters the Acting stage. This is when decisions are translated into concrete results. The agent interacts with the digital or physical world to accomplish its task.

Forms of action include:

Content Generation: Creating text, speech, alerts, or video.

Data Interaction: Reading or writing information to databases.

Real-World Control: Executing physical commands through actuators — like an autonomous car controlling steering and speed.

The Evolution Engine: Feedback Loop

More than a simple component, the feedback loop is the engine that drives an AI agent’s evolution. It’s the continuous learning mechanism that transforms a static system into a dynamic, adaptive tool.

Improvement mechanisms include:

Self-Assessment: The agent autonomously monitors the results of its actions. It evaluates whether each step brought it closer to or further from the objective and makes course corrections independently.

RLHF (Reinforcement Learning with Human Feedback): The agent receives external feedback from humans, typically through simple evaluations like “thumbs up” or “thumbs down.” In 2025, RLHF became the standard alignment strategy for LLMs.

Practical Example: Travel Booking

Consider an agent responsible for scheduling corporate travel:

Sensing: The user provides dates and destination via chatbot. The agent captures these inputs.
Thinking: The agent processes the request by consulting the Knowledge Base (user preferences), Policy Information (spending limits, preferred partners), and external data (prices and availability).
Acting: The agent interacts with booking systems, completes the reservation, and delivers the e-ticket and confirmation.
Feedback Loop: The agent asks “How did I do?” and receives feedback that feeds the learning cycle.

The result? An increasingly personalized, intelligent, and effective agent.

The Human-Machine Dialogue

Understanding this anatomy enables us to use agents strategically. Effective interaction with an AI agent is not a monologue, but a dialogue.

Your role in this dialogue:

In Sensing: Provide clear, well-structured inputs.
In Thinking: Define context, rules, and objectives.
In Feedback: Offer corrective feedback consistently.

By doing this, you become a co-pilot in the decision-making process. The result is focus on higher-value strategic and creative tasks, while the agent handles the details with increasing speed and precision.

Victorino Group implements AI agents with built-in governance for companies that cannot afford to fail. If you want to explore how to apply this architecture to your processes, let’s talk.