sids blog

Artificial General Intelligence (AGI) is one of humanity’s most ambitious frontiers — the quest to build machines that can think, learn, and adapt like humans.

AGI is mimicking human intelligence.

An AGI must be able to learn everything a human can and apply knowledge from one domain to another. For example, if an AGI learns how to play chess, it should be able to apply strategic thinking to solve problems in business. That’s learning transfer.

To get AGI right, we need to master autonomy, self-improvement, truth-seeking, learning transfer, and sensors and actuators.

• Autonomy – The ability to act without human assistance.
• Self-Improvement – Meta-learning: knowing how to learn and the ability to learn anything.
• Truth-Seeking – Reasoning through first principles or reasoning grounded in reality.
• Learning Transfer – The ability to transfer principles learned in one domain to another.
• Sensors and Actuators – The ability to interact with the real world in real-time.

AI agents are the implementation of the autonomy principle. Current LLMs represent static intelligence; they are like geniuses who never speak or act. AGI is active intelligence, and to convert static intelligence into active intelligence, agency (the ability to act independently) is essential. That’s where AI agents come in.

What is Agentic AI?

Agentic AI is AI that can perform tasks without any human assistance. There are different types of AI agents, such as Simple Reflex Agents, Model-Based Reflex Agents, Goal-Oriented Agents, Utility Agents, and Learning Agents. These agents are capable of interacting with the environment, receiving real-time data through sensors, processing that data, making the best possible decisions according to the input and pre-trained models, performing actions, receiving feedback, and learning through that feedback.

• Simple Reflex Agent – Reacts to input in the moment. It has no memory. For example: a thermostat that turns on the heater if it’s cold.
• Model-Based Reflex Agent – Reacts based on the history of real-time input and a pre-trained knowledge base. However, it lacks future planning. For example: a robot vacuum cleaner that remembers your room layout.
• Goal-Oriented Agent – Can plan future actions but may struggle with optimization. For example: Google Maps planning your route.
• Utility Agent – Can figure out the best possible action to achieve a goal but cannot self-improve. For example: a self-driving car choosing the fastest and safest route.
• Learning Agent – Learns from experience. For example: Instagram’s Explore feed that improves based on your behavior.

Learning Agents are closer to AGI because they can learn and adjust their behavior based on the feedback they receive.

Why is Agency Core to AGI?

The goal of AGI is to develop a system that can do everything a human can do. Human intelligence isn’t just about storing information; it’s about setting goals, making decisions, adapting behavior, and functioning in uncertain environments. So, a true AGI must operate in a world that requires agency — the ability to set goals, take action, and pursue those goals over time.

Agency is what turns raw intelligence into adaptive, purposeful behavior — the very essence of AGI.

Architecture of Agentic AGI

To design Agentic AGI, we need a cognitive core, memory module, planner, sensors, actuators, learning engine, and a safety, ethics, and value alignment layer.

• Cognitive Core – The brain of the agent that reasons and solves problems. LLMs serve as the reasoning mind. They are the knowledge base for agents.
• Memory Module – Remembers past actions, feedback, and preferences.
• Planner – Sets goals, breaks them into subgoals, and figures out the best possible actions. It is the decision center of Agentic AGI.
• Actuators – The interface through which the agent interacts with both physical and virtual environments. These are like the hands and feet of the agents.
• Sensors – Collect feedback that offers critical information for improvement. These are like the eyes and ears of the agents.
• Learning Engine – Updates the agent’s understanding of the world.
• Safety Layer – Ensures that Agentic AGI’s values align with human values.

Designing AGI is not just a technical challenge — it’s a philosophical one. We are not just building smart systems. We’re building minds.