How Agentic AI Works: A Simple Breakdown

Now that you know at a high level what Agentic AI is and how it is different from basic AI, it’s time to take a peek behind the curtain to see how it works.

Unlike traditional AI systems that primarily react to commands, Agentic AI can understand goals, plan actions, and adapt to changing environments. At a high level, Agentic AI operates through three fundamental processes to function autonomously in real-world environments: perception, planning, and action.

1. Perception: Understanding the Environment

Perception is the ability of the AI to interpret its environment using various sensors or data inputs. These can include:

• Computer Vision: Cameras and image recognition algorithms help the AI perceive physical surroundings using techniques such as convolutional neural networks or CNNs.

• Natural Language Processing (NLP): Enables the AI to understand and process human language. These include user instructions (parsed using natural language understanding, NLU), preferences (stored in knowledge graphs or vector databases), and feedback (processed via reinforcement learning signals).

• Sensor Data Fusion: Combines inputs from multiple sensors, such as LiDAR, GPS, and IoT devices, to build a coherent 3D model of the environment.

• Knowledge Graphs and Databases: Provides contextual information to improve decision-making by ingesting and analyzing data from various sources like social media feeds (processed using NLP techniques like sentiment analysis and entity recognition), news articles (analyzed via topic modeling and summarization algorithms), financial markets (time-series analysis, predictive modeling), and weather reports (using meteorological data APIs and forecasting models).

This information is then processed and interpreted to create a structured representation of the environment within the AI system.

Perception is crucial because it forms the basis for all subsequent actions. Advanced AI models like transformer-based architectures (e.g., GPT models) process vast amounts of unstructured data to recognize patterns and predict outcomes.

2. Planning: Decision-Making and Strategy

Once an Agentic AI has perceived its environment, it needs to decide what actions to take. Planning involves:

• Goal Formulation: The AI determines its objectives based on user input, predefined missions, or self-generated insights. Goals are often represented as formal logic statements or reward functions in reinforcement learning.

• State Representation: It constructs a model of the world using structured data representations. Markov Decision Processes (MDPs) model sequential decision-making while graph-based structures model complex relational mappings.

• Option Generation & Evaluation: The AI generates multiple possible courses of action using Symbolic AI methods for logic-driven choices, reinforcement learning for exploratory decision-making and evolutionary algorithms to iteratively optimize actions. Each option is assessed based on predicted effectiveness by exploring future possibilities using Monte Carlo Tree Search (MCTS), cost-benefit analysis, utility functions or value networks.

• Path Optimization: This consists of decomposing the goal down into smaller, manageable steps Algorithms such as A* search to find shortest path, Q-learning and deep reinforcement learning (RL) for dynamic environments, and multi-agent coordination are employed to identify optimal sequences of actions.

• Uncertainty Handling: Probabilistic models, such as Bayesian Networks, help manage uncertainty and incomplete information.

Planning is what differentiates Agentic AI from conventional AI models. Instead of following a strict set of rules, it continuously updates its strategy based on new data, much like a human making decisions in real time.

3. Action: Executing Decisions in Real-Time

Action is the final step where AI translates plans into tangible outputs. This stage involves:

• Interacting with and Automating Software Systems: Digital agents can autonomously perform complex workflows, including API calls (RESTful, GraphQL or asynchronous), database calls (SQL, NoSQL queries), inter-process communication or message queues, automating transactions or generating content.

• Controlling physical devices: For robots, this could involve sending control signals to actuators, motors, and other hardware components, using real-time operating systems (RTOS) and communication protocols like ROS (Robot Operating System) for movement, grasping or manipulation.

• Feedback Mechanisms: Reinforcement learning (RL) enables the AI to learn from its actions by assessing outcomes and adjusting future behavior accordingly.

By continuously iterating between perception, planning, and action, Agentic AI achieves adaptability and problem-solving capabilities previously seen only in human cognition.

The three stages discussed above lead to the following key concepts for Agentic AI:

• Goals: A goal is a desired outcome or objective the AI aims to achieve. Goals can be externally set (by users) or autonomously generated based on the system’s optimization functions. Multi-agent systems involve collaborative or competing goals among different AI entities. Represented as formal logic, reward functions, or state-action pairs in RL.

• Environment: The environment consists of all external factors that influence decision-making. It can be static or dynamic. Encoded as state vectors, knowledge graphs, or simulated environments.

• Actions: Actions are steps an AI takes to interact with its environment. Actions can be discrete or continuous, represented as control signals or API calls.

Example of Agentic AI in Financial Closing:

That was a whirlwind tour of the inner workings of Agentic AI with the smorgasbord of techniques utilized in each of its steps. Let’s put this together in the context of an enterprise Agentic AI use-case.

An AI-driven financial close assistant automates month-end reporting for a company. It collects transaction data, reconciles discrepancies, and detects anomalies (Perception). It then prioritizes open issues, assigns tasks to the right finance teams, and suggests corrective actions based on historical trends and compliance rules (Planning). Finally, it executes reconciliations, generates reports, and escalates unresolved issues, continuously learning from past errors to improve future closings (Action). If unexpected discrepancies arise, it dynamically adjusts the process, ensuring timely and accurate financial statements—just like an intelligent, self-improving accounting assistant.

Conclusion

Agentic AI represents a transformative leap in artificial intelligence, enabling systems to act autonomously with a level of adaptability that traditional AI lacks. By leveraging perception, planning, and action in a continuous loop, Agentic AI can operate in dynamic environments, optimize decision-making, and achieve complex goals with minimal human intervention.

As this technology advances, we can expect to see applications spanning from autonomous robotics to intelligent enterprise automation, revolutionizing industries across the board.