When AI Gets a Game Plan: How Agents Think Ahead
- Angie Okhupe
- Sep 10
- 3 min read
Alright, let's pick up where we left off! We've been on a mission to figure out what makes an AI feel genuinely smart. We decided it's not just a vault of data, but a set of skills—like a digital Swiss Army knife. Back in Part 1, we talked about the secret sauce that makes an AI smart. It’s not just a digital brain stuffed with data—it’s the skills that let it use that data:
Memory: remembering what we talked about last time
Planning: breaking big, hairy tasks into steps and adapting when things change
Tool use: knowing when to call in help
Teamwork: playing well with others
In Part 2, we unpacked memory—the difference between a forgetful goldfish and a super librarian. Now, it’s time to zoom in on the next ingredient: planning.
From Reacting to Anticipating
Most basic AI is like a fast-food order taker. You ask for a cheeseburger, they give you a cheeseburger. Transaction complete.
Agentic AI, on the other hand, is more like a personal chef. You say, "I'd like a healthy, romantic meal for two this Friday night." The chef doesn't just hand you a tomato. They plan the menu, check what's in the fridge, make a shopping list, and sequence the cooking so everything is ready at the same time. That ability to plan ahead is what turns a simple order into a multi-step project aimed at a delicious goal.
So, How Does an AI Actually Plan?
Don't imagine a robot staring at a calendar with a cup of coffee. AI planning is less about visionary insight and more about brilliant, mechanical breakdown. Here’s how it often works:
Goal Setting: First, it translates your vague wish into a clear, internal objective. "Okay," it thinks, "success looks like a confirmed flight, hotel, and itinerary for a 7-day trip to Tokyo."
Task Decomposition: This is the core of the magic. It takes that huge goal and slices it into bite-sized, actionable steps. It’s like writing a to-do list:
Step 1: Research the best time to visit Japan.
Step 2: Find flights within the budget.
Step 3: Look for hotels in a central neighborhood.
(and so on...).
Sequencing: Now, it puts those steps in order. Some things have to come first—you can't book a hotel before you know your dates. It figures out the logical flow.
Simulation (The "What If" Phase): Before it charges ahead, a smart agent might do a quick mental dry-run. "If I search for flights now without knowing the dates, that will probably fail. I should start with step 1."
Adaptation (The "Oops" Fixer): This is the cool part. As it executes the plan, it constantly checks in: "Did that step work? Did I get a useful result?" If something fails, it doesn't just give up. It loops back, tweaks the plan, and tries a different path. It learns from stumbles in real-time.
How Planning and Memory Work Together
This is where it all clicks. Memory and planning are a powerhouse duo.
Memory is about the past. It says, "Hey, last time I tried to book a flight on that website, it asked for a passport number first."
Planning is about the future. It says, "Noted! So for this booking, I'll gather the passport info before I start clicking."
An AI with memory but no planning is a brilliant historian who can't change the future. An AI with planning but no memory is an eager strategist who keeps making the same mistakes. Together, they start to look... well, smart.
Why This is a Game-Changer
This shift is huge. It transforms AI from a tool you micromanage into a partner you brief. You don't have to say, "First google flights, then check Kayak, then..." You just say, "Book me a trip," and trust it to build the roadmap. That’s a whole new level of helpfulness.
Up next, we'll look at the third skill in our series: Tool Use. Because truly smart beings, whether human or AI, know that the real power isn't in knowing everything yourself—it's in knowing how to use the right tool for the job.
Bonus Fun Fact
The guts of modern AI planning owe a lot to a 1970s system called STRIPS (Stanford Research Institute Problem Solver). It was basically a fancy flowchart: "Here's where I am, there's where I want to be, what actions fill the gap?" The math has gotten infinitely more complex, but the core idea—breaking down a problem—hasn't changed. Some good ideas are timeless.
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