AI is More Than Just Following Instructions
How Agentic AI Thinks, Makes Decisions, Innovates Beyond Simple Instruction & Not Limited For-Loops & IF/Else Commands
Many people think AI only does what it is programmed to do. At first, this makes sense. Humans create AI, so it seems logical that its behavior is entirely determined by code.
This view misses a crucial point.
Modern AI, especially agentic AI, behaves in ways that cannot be explained by examining its code line by line.
In this edition, let me share why AI can act in complex, emergent, and strategic ways.
The Misconception
Thinking AI is a Giant If-And-Else Machine.
For a long time, AI was simple. Early programs followed rules like this:
If the user asks X, do Y.
Otherwise, do Z.
Modern AI works very differently. It is trained on massive amounts of data and learns patterns, rather than following explicit instructions for every possible situation. For example, a language model like GPT-5 mini was not programmed with responses for every question. Instead, it was trained on billions of examples to learn how language works. The instructions written by developers define how the AI learns, not the exact words it will produce.
Emergence
Behaviour beyond Programming
AI can display behavior that was never explicitly programmed. Examples include:
Summarizing text and generating analogies.
Negotiating or strategizing to complete multi-step tasks.
Planning several steps ahead to reach a goal without being told exactly how.
This behavior arises from learning patterns in data and using optimization algorithms to achieve objectives.
Humans offer a useful analogy. Our DNA does not contain instructions to invent calculus or play the violin, yet humans can do these things. AI can similarly develop skills that were not directly programmed.
What is Agentic AI?
Agentic AI refers to systems that can:
Set sub-goals
Plan multiple steps ahead
Adjust behavior based on feedback from their environment
These systems are not just following rules. They act like agents that make decisions and pursue objectives.
Technical Perspective
Agentic AI often combines:
Large Pretrained Models trained on extensive datasets to learn patterns.
Goal-Oriented Reinforcement Learning, where the AI is rewarded for achieving outcomes rather than following fixed rules.
Planning and Memory Modules that allow reasoning across multiple steps and predicting consequences before acting.
For instance, a warehouse robot with agentic AI does more than follow a path. It observes obstacles, predicts worker movements, and plans the most efficient route to pick up packages without human guidance.
Why AI is Not Just Following Instructions
Even though AI is designed with certain goals, how it achieves them is not predetermined. Key points include:
Complexity is Non-Linear
Small changes in input can produce very different outputs.Learning Creates Novel Patterns
AI can generate solutions that were not in its training data.
Multi-Step Reasoning
Agentic AI can chain decisions, producing behavior that may seem creative or strategic.
A chess engine illustrates this. The rules of chess are fixed, but the strategies it develops are discovered, not programmed.
An Example
A simple analogy is teaching a child by showing them thousands of examples of stories, conversations, and problem-solving.
You do not write a script for what they will say. Over time, they learn patterns and begin generating original responses. Modern AI works in a similar way. It is constrained by goals and data, but its behavior is not fully determined by explicit instructions.
Why This Matters?
Understanding this distinction is important because it:
Explains why AI can be unpredictable
Shows why agentic AI requires careful oversight, especially when interacting with humans or real-world systems
Helps anticipate both opportunities and risks, instead of underestimating AI by assuming it is a simple rule-follower
Closing Thought
Saying AI just follows instructions is like saying a jazz musician just follows sheet music. The framework exists, but the improvisation and creativity that emerge go far beyond simple programming. Agentic AI can learn, adapt, and act in ways that surprise even its creators.
AI is not magic and not sentient yet, but it is also far more than a set of rules. It is a complex system capable of acting, adapting, and generating outcomes that cannot be traced line by line to a programmer’s code.