Agency levels: What’s the Difference?

Let’s keep it simple:

• Generative AI works like this: You write a prompt, and it gives you something. Maybe a picture, maybe text.
• Reactive Agents wait for something to happen. Then they do a thing. No hard thinking ahead.
• Agentic AI is different. It starts with a goal. Then it decides what to do to reach it.

It’s not just reacting. It’s planning. And doing. That’s a big shift.

Main Parts of Agentic AI

Here are the four key areas:

1. Understanding the Goal

Agentic AI tries to figure out what you want. It doesn’t just look at words.

• Understands goals written in natural language
• Looks at what’s happening around it
• Can guess the real goal even if not said directly
• Balances different people’s needs if needed

Hard to do? Yeah. But needed.

2. Planning and Reasoning

Once the goal is known, it breaks the work into steps.

• Divides big tasks into smaller ones
• Uses resources in smart way
• Makes backup plans
• Thinks about time and order of tasks

Helps when timing matters. And usually, it does.

3. Doing the Work (Autonomous Execution)

Now it starts working, without waiting for orders.

• Picks the right tools
• Uses APIs, systems
• Tries again if something fails
• Keeps track of what it’s doing

Mistake it makes? Try again it will.

4. Learning and Improving

After doing things, it looks back and tries to do better next time.

• Watches results
• Adjusts plans
• Builds its own knowledge
• Learns what you like or don’t like

Step by step, smarter it gets.

What Makes Agentic AI Special?

Here’s what really separates it from others:

Stays On Track (Persistence)

• Remembers what happened before
• Works on long-term tasks
• Can pause and continue later

Leave it alone? Back it will be.

Makes Its Own Choices (Agency)

• Chooses what to do without being told
• Asks for help when confused
• Manages limits like time or money
• Checks how well it’s doing

Kind of like a helpful coworker.

Knows Tools and Uses Them Well

• Finds out what tools it has
• Connects tools to finish tasks
• Builds new tool chains if needed
• Picks best one for the job

Too many tools? Not a problem.

Real-Life Examples

Need real examples? Let’s see:

Software Agent
Goal: Make the API faster
Steps:

• Looks at code
• Finds slow parts
• Suggests better code
• Tests the changes
• Watches the results

Quiet but busy.

Personal Assistant Agent
Goal: Get ready for your conference
Steps:

• Books flights
• Sets meetings
• Prepares slides
• Sends reminders
• Handles small things
• Welcomes feedback from the user

One less thing to worry about.

Business Agent
Goal: Find new market ideas
Steps:

• Checks out competitors
• Looks at market data
• Spots product gaps
• Writes strategy plans
• Schedules meetings
• A/B tests

No tie needed.

How It Works (architecture)

Here’s how parts fit together:

Goal → Plan → Do → Watch
   ↑    ↓    ↓
[Memory] ← Learn ← Feedback

What’s Hard About It?

Matching Goals to Real Needs (Alignment)

• Makes sure AI does what you really want
• Handles unclear or mixed goals
• Avoids tricks or bad behavior

What more can I say?

Staying Safe (Control)

• Keeps work in limits
• Lets people check the work
• Can roll back changes
• Leaves clear records

Can anything else go wrong?

Managing Resources

• Keeps API costs low
• Spends time wisely
• Balances what’s most important
• Knows its usage limits

Not free, this stuff.

Plan, act, learn

Agentic AI isn’t magic.
It plans. It acts. It learns.
Not just reactive. It’s useful.

Was it all worth it? You tell me.