Phase 1 (Now): Agent + Skills
Phase 2 (Next): Memory + Self-learning

Phase 1: Agent + Skills

Large models are no longer just “answering questions.”
They are starting to take action.

AI agents can:

• Browse websites
• Fill forms
• Book tickets
• Execute workflows
• Operate software systems

The focus today is teaching AI practical skills so it can complete real-world tasks.
This marks a shift from advisor to executor.

However, current agents still struggle with generalization.
They often work well in one environment but fail when interfaces or workflows change.
Like humans, AI needs more exposure to diverse scenarios to adapt.

Phase 2: Memory + Self-learning

The next breakthrough is not just doing more tasks —
it’s learning from them.

Future AI systems must:

• Store long-term memory
• Recall past experiences
• Evaluate their own performance
• Learn from mistakes
• Improve continuously

Today’s models rely on expensive retraining cycles.
Tomorrow’s models will evolve through self-learning.

This shift turns AI from a tool into a growing system.

The Bigger Picture

AI’s purpose is not to create new apps.
It is to replace human labor more efficiently.

Chat-based products didn’t create new needs —
they improved how we search, communicate, and get information.

As agents gain memory and learning abilities,
AI will begin replacing entire job functions.

In short:
Now, AI learns how to work.
Next, AI will learn how to grow.

The real transformation has only just begun.