At this point, you’ve seen the progression:
- Prompts → getting better outputs
- Debugging → fixing failures
- ReAct → enabling actions
- Tool calling → separating decision and execution
Now comes the real shift.
You’re no longer writing prompts.
You’re designing decision loops.
The Mental Shift
Most people stay here:
Input → Model → Output
That’s a single-step interaction.
But real-world problems don’t look like that.
They look like:
- Gather information
- Decide next step
- Take action
- Evaluate result
- Repeat
That’s a loop.
What Is an Agent, Really?
Forget the hype.
An agent is just:
A system that uses a model to make decisions in a loop until a goal is reached.
That’s it.
The Core Loop
Every agent follows this pattern:
- Observe → What’s the current state?
- Think → What should I do next?
- Act → Take a step (tool, API, decision)
- Evaluate → Did it work?
- Repeat
This is just ReAct extended into a full system.
A Concrete Example
Let’s say you want to build:
“Find the best laptop under $1500 and justify the choice”
Step 1: Observe
- No data yet
Step 2: Think
- I need laptop options
Step 3: Act
- Call product API
Step 4: Observe
- Get list of laptops
Step 5: Think
- Filter by price and specs
Step 6: Act
- Apply filtering logic
Step 7: Observe
- Shortlist created
Step 8: Think
- Compare tradeoffs
Step 9: Act
- Generate evaluation
Step 10: Final Answer
- Recommend best option
This is not a prompt.
This is a multi-step decision system.
Where Most People Get It Wrong
1. Treating Agents Like Magic
They think:
“The model will figure it out”
It won’t.
You need to design:
- Steps
- Constraints
- Tool boundaries
2. No Loop Control
When does the agent stop?
- After 1 step?
- After 5 steps?
- When confidence is high?
If you don’t define this, you get:
- infinite loops
- shallow answers
3. Weak State Management
Agents need memory:
- What has been done?
- What remains?
- What failed?
Without state → no continuity.
Designing a Good Agent
Here’s a practical checklist.
1. Define the Goal Clearly
Bad:
Help user with shopping
Good:
Recommend 3 laptops under $1500 with pros/cons
2. Break Into Steps
Don’t rely on the model to invent structure.
Define:
- search
- filter
- compare
- summarize
3. Design Tools Carefully
Tools should be:
- specific
- predictable
- composable
Bad:
process_data(data)Good:
get_products(category, price_limit)
filter_products(criteria)
compare_products(list)4. Control the Loop
Decide:
- max steps
- stopping condition
- fallback behavior
5. Add Evaluation
After each step:
“Did this move us closer to the goal?”
If not → adjust.
The Big Picture
Let’s connect everything:
- Prompts → control outputs
- Debugging → fix failures
- ReAct → enable actions
- Tool calling → separate execution
- Agents → orchestrate decisions
This is the stack.
Why This Matters for You
This is where your background becomes leverage.
Most people:
- learn prompts
- stop there
You:
- think in systems
- design flows
- define constraints
That’s what makes the difference between:
👉 “using AI”
👉 “building AI systems”
Final Thought
If you remember one thing, make it this:
The model is not the system.
It’s just one component inside it.
Your job is to design everything around it.