In this tutorial, we’ll build AI agents that can think, remember and adapt, whether they’re controlling robots or acting as The post Build AI Game Characters and Robots That Outsmart You appeared first on The New Stack.
In this tutorial, we’ll build AI agents that can think, remember and adapt, whether they’re controlling robots or acting as characters in games. These aren’t your typical chatbots or scripted non-player characters (NPCs).
Why This Matters
Most AI in games and robotics today is fairly limited. NPCs follow basic scripts, robots execute pre-programmed routines and when something unexpected happens, they struggle to adapt.
But what if your game characters could actually learn from conversations with players? What if robots could figure out new solutions when their original plan doesn’t work? That’s exactly what we’re building here.
I’ve been working with LLMs in interactive environments for a while now, and the potential is honestly incredible. We’re talking about robots that get smarter every time they bump into a wall, and game characters that remember your name months later.
A Real Example: Building a Smart Game Guide
Let’s build an NPC for a game or simulation that acts as your personal guide. This is an AI that genuinely gets better at helping you over time.
Here’s what makes it special: When it first meets you, it might give you basic directions through a maze. But after watching you struggle with certain areas, it starts offering more specific tips. If it sees you consistently missing a hidden passage, it’ll start mentioning it earlier. When you come back to play again weeks later, it remembers your play style and adapts accordingly.
How Agentic AI Actually Works
Traditional game AI and robot programming works like this: “If player does X, then do Y.” It’s rigid and predictable.
Agentic AI is different. These systems can reason through problems, maintain long-term memory, and most importantly, they can reflect on their own performance and improve. When an agentic robot hits a dead end, it doesn’t just turn around — it updates its understanding of the environment and plans a better route next time.
The key differences:
They make decisions based on reasoning, not just rules. They remember everything and use that memory to get better. They can critique their own performance and adapt. They handle unexpected situations without breaking.
What We’re Actually Building
The demo NPC lives in a simulated world (think Unity or Webots) and does these things:
It greets players naturally and starts building a relationship. When guiding you through areas, it pays attention to where you get stuck and offers increasingly helpful advice. Every time it fails to help you effectively, it takes notes and tries a different approach next time. It builds up a mental map of not just the physical space, but how different players like to navigate it.
The Technical Setup
This is more straightforward than it sounds. There are five main pieces:
The brain is a large language model (LLM) like GPT-4 that handles all the thinking and language processing. The interface connects our AI to whatever world it’s living in, such as your game engine or robot simulator. The event processor takes things that happen in the world (player moved, robot hit wall, conversation started) and turns them into something the AI can understand. The memory bank stores everything the AI learns, from conversations to successful strategies, failed attempts and map updates. The learning loop is where AI regularly looks back at what worked and what didn’t, then updates its approach.
The beautiful thing about this setup is that once you get it running, the AI starts getting noticeably better at its job without you having to program new behaviors manually.
Think of it less like traditional programming and more like training a very fast learner who never forgets anything.
Step 1: Install Required Libraries
pip install langchain openai llama-index
For Unity, use Python communication via Unity ML-Agents or socket server.
Step 2: Set Up a Simple Unity Environment:
Create a maze scene Add a “GuideBot” NPC with a Python socket listener Define trigger zones (e.g., entry, exit, wrong turn)
Step 3: Create the Python Agent Controller
# Optional: A map tool to track progress
Step 4: Define Agent Behavior for Game Events
Example Events
“player entered maze” “bot hit wall” “player reached dead-end” “exit found”
Each event passes relevant coordinates or map segments as context.
Step 5: Enable Bi-Directional Communication In Unity (C#):
Step 6: Add Reflection and Learning Capability
When the bot fails or succeeds, the agent updates its future guidance strategy accordingly.
Step 7: Enhance Immersion With Agent Personality.
Add an agent profile with traits:
Advanced Extensions
Dynamic memory graphs: Use vector databases such as, FAISS or Weaviate to manage spatial memory. Voice interaction: Integrate with ElevenLabs or Amazon Polly for immersive audio. Adaptive emotion models: Modify tone based on player frustration or excitement. Real robot deployment: Move from simulation to Robot Operating System (ROS)-based robot navigation using the same agent logic.
Why This Approach Works
Traditional NPCs and robots operate on predefined scripts or rigid path-planning. In contrast, agentic AI enables improvisation. The agent:
Understands the player’s actions semantically Reflects on failed attempts Stores history across sessions Communicates with adaptive tone and content
This leads to more immersive gameplay and more intelligent robot behavior.
Where This Takes Us
This is just the beginning. I’ve watched these systems evolve over the past few years, and the trajectory is remarkable. We’re moving from characters that feel like sophisticated chatbots to ones that genuinely surprise you with their responses. The robot applications are even more exciting: Imagine maintenance robots that don’t just follow repair manuals but actually understand the systems they’re working on.
The shift from scripted behaviors to genuine reasoning changes everything. Players start forming real attachments to NPCs because the interactions feel authentic. Robots become actual collaborators rather than just programmable tools.
We’re building the foundation for AI that grows with us. Your game companion doesn’t just reset between sessions — it builds on every conversation. Your robotic assistant doesn’t just execute tasks — it understands the context and purpose behind what you’re trying to accomplish.
LLMs have gotten reliable, the simulation environments are robust, and the integration points exist. We’re not waiting for some future breakthrough — the pieces are all here.
So if you’ve been thinking about experimenting with agentic AI, stop thinking and start building. The most interesting applications are going to come from developers who get their hands dirty with these systems now, while there’s still room to define what intelligent interaction actually looks like.
Ready to build self-improving AI agents that think in loops, not just prompts? Read Andela’s article,” Inside the Architecture of Self-Improving LLM Agents.”