Do you wonder how agentic AI can perform cognitive tasks like the human brain? What language model drives agentic AI? What if a machine's cognitive functionality could be achieved beyond what we see today?
With the emergence and excitement surrounding Agentic AI, these questions are obvious for anyone seriously interested in leveraging technology to enhance their workflow or daily activities. The growing interest in creating intelligent systems that can act and make decisions independently began to show more prominent results with the advancement of machine learning and large language models. Building agentic AI systems capable of planning, autonomous decision-making, and adapting to the environment require advanced technologies.
Let us delve into human brain function to gain a clearer understanding of how Agentic AI works and directly correlate to its functions. In human reference, cognitive functions are the brain's mental functions that aids in learning, thinking, remembering, and acting. These include perception, attention, memory, language and planning, reasoning & decision-making. In the simplest terms, the human brain achieves this using six main regions of the brain that manage and control cognitive functions. These regions can be categorized as upper front-top, upper back-top, upper front-rear, bottom middle, bottom back, and a central controller.
The diagram illustrates the functions of the human brain here.
Comparison of cognitive functions supported by brain regions and those mimicked by agentic AI.
Human Cognitive Function | Brain Part | Agentic AI |
Planning, reasoning, and decision-making | upper front-top (front area of brain) | Machine learning & Deep Learning algorithms Planning- MonteCarloTree Search, Graph Search , Reasoning - Logical or probabilistic reasoning (Bayesian network), knowledge data , Decision Making- Reinforcement Learning, Markov Decision Processes. These process might use LLM |
Perceive information from senses (Sound, touch, sight) | upper back-top (ear, hand, reading, signals to brain) | Audio processing & RNN (audio sensors), speech recognition), Tactile Sensors |
Perception, object & Image recognition | upper front-rear | Computer vision & CNN, Chemical sensors, Motion and position sensors |
Learning & Memory | bottom middle (through experience, repetition, and emotions , short, long memories | Supervised learning, Unsupervised, Reinforcement trial & error learning, self supervised training data, optimization, and updates. AI systems can scale very high and do not forget information until specified, Structured database, Neural Networks, knowledge graphs, vector database. Pre-training on large data to learn patterns, contextual learning via transformer, real time learning |
Coordination and balance | bottom back | Predictive analytics, Real0time data stream process, API integration |
Automatic & Involuntary Responses (Instant reaction) | central controller | AI internal monitoring, Reflex action, Feedback loop, Sensor based action, Reinforcement learning reward |
Diagram illustrating the modular architecture of Agentic AI designed to perform brain-like functions.
This is a simplified architecture of agentic AI. To put it together, an agentic AI system combines these modules using advanced technologies into a unified architecture. For example, a Data Architect AI Agent responsible for designing a database may employ natural language processing NLP to comprehend user queries, machine learning algorithms for classification of data, pattern detection, and schema recommendation, reinforcement learning to create optimal design over time, training data from database, documents, graphs, feature, vectors to store new information all while keeping track of previous interactions making it a valuable tool for automated database architecture and management.
Agentic AI has successfully mimicked the intellect of the head brain. While this is permissible, we should stop here and refrain from reproducing the emotions of the heart-brain or the intuition of the gut-brain in AI machines.
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