You don't learn to build agents by tackling everything at once. You learn by mastering one skill at a time, in environments designed to teach that specific skill.
Agent Arena's scenarios are a curriculum. Each one focuses on particular agentic capabilities, with complexity that increases as your skills develop. This isn't gamification for its own sake — it's structured learning that builds durable understanding.
The Four Tiers
Agent Arena organizes scenarios into four tiers, each introducing new concepts while building on previous skills.
Tier 1: Foundations
These scenarios teach the basics: how agents perceive, decide, and act.
Simple Navigation
- Move from point A to point B
- Handle obstacles in the path
- Learn basic observation processing and tool use
- Concepts: Perception, basic tool calling, movement
Foraging
- Collect resources scattered across the environment
- Prioritize between multiple targets
- Avoid hazards while gathering
- Concepts: Goal-directed behavior, resource detection, basic planning
Obstacle Course
- Navigate through constrained spaces
- Sequence movements to reach goals
- Handle dead ends and backtracking
- Concepts: Spatial reasoning, sequential decision-making, recovery from mistakes
After Tier 1, you understand the basic observe-decide-act loop and can build agents that accomplish simple goals.
Tier 2: Memory and Planning
These scenarios require agents to remember and think ahead.
Crafting Chain
- Collect resources in dependency order (wood → planks → table)
- Manage inventory across multiple steps
- Plan collection sequences before acting
- Concepts: Multi-step planning, dependency resolution, inventory management
Scavenger Hunt
- Find items scattered across a large map
- Remember locations visited and items found
- Return to previous locations when needed
- Concepts: Long-term memory, deferred goals, spatial memory
Maze Exploration
- Navigate unknown maze structures
- Build mental maps while exploring
- Use memory to avoid revisiting dead ends
- Concepts: Map building, memory-augmented navigation, exploration strategies
After Tier 2, your agents can plan over multiple steps and use memory effectively.
Tier 3: Adversarial and Dynamic
These scenarios add pressure: the world changes while you act, and it doesn't always cooperate.
Predator Evasion
- Collect resources while avoiding moving hazards
- Balance risk and reward
- Replan when threats appear
- Concepts: Reactive planning, risk assessment, dynamic replanning
Resource Competition
- Compete against other agents for limited resources
- Model opponent behavior
- Adapt strategy based on competition
- Concepts: Opponent modeling, strategic behavior, adaptive planning
Tower Defense
- Make decisions under time pressure
- Prioritize threats and responses
- Manage resources during active challenges
- Concepts: Real-time decision-making, prioritization, resource management under pressure
After Tier 3, your agents can handle dynamic environments and adversarial conditions.
Tier 4: Multi-Agent Cooperation
These scenarios require multiple agents to work together.
Team Capture
- Coordinate multiple agents to capture targets
- Assign roles and divide territory
- Communicate and synchronize actions
- Concepts: Communication protocols, role assignment, coordinated action
Collaborative Building
- Multiple agents contribute to shared construction
- Negotiate who does what
- Handle conflicts and overlapping efforts
- Concepts: Shared goals, task distribution, conflict resolution
Relay Race
- Agents must hand off resources or tasks
- Timing and positioning matter
- Trust between agents is required
- Concepts: Handoffs, timing, inter-agent trust
After Tier 4, you can design multi-agent systems with communication and coordination.
Why Scenarios Work for Learning
Clear Objectives
Each scenario has explicit success criteria. Did you collect all the resources? Did you reach the goal? Did your team capture the targets?
This clarity matters. You know whether your agent succeeded or failed. There's no ambiguity about what "good" looks like.
Measurable Progress
Scenarios provide metrics: resources collected, time taken, efficiency scores. You can compare your agent's performance across runs and against benchmarks.
This lets you verify that changes improve behavior, not just change it.
Focused Learning
Each scenario emphasizes specific skills. If you're struggling with memory management, the Scavenger Hunt scenario isolates that skill. If planning is the problem, Crafting Chain focuses on that.
You can identify weak areas and practice them directly.
Intentional Failure Modes
Scenarios are designed with specific ways to fail:
- Foraging agents that don't prioritize will miss resources
- Maze explorers without memory will loop forever
- Competing agents that don't model opponents will lose
These aren't bugs — they're lessons. Each failure mode teaches something about what agents need to handle.
Benchmarks and Lessons
Every scenario serves two purposes: it's a lesson (teaching specific skills) and a benchmark (measuring agent capability).
As Agent Arena grows, community agents can be compared on standardized scenarios. This creates shared understanding of what "good" looks like for different agent architectures.
The Layered Interface
Not everyone starts at the same level. Agent Arena provides three interface layers so you can learn at your current skill level.
Layer 1: Simple (Beginners)
Minimal boilerplate. You write a class with a decide method that returns a tool name:
class MyAgent(SimpleAgentBehavior):
system_prompt = "You are a foraging agent. Collect apples."
def decide(self, context: SimpleContext) -> str:
if context.nearby_resources:
return "move_to"
return "idle"
The framework handles prompts, memory, and tool execution. You focus on decision logic.
Best for: Your first agents. Understanding the basic loop.
Layer 2: Intermediate (LLM Integration)
You control more: prompts, memory strategy, tool selection:
class MyAgent(AgentBehavior):
def __init__(self, backend):
self.backend = backend
self.memory = SlidingWindowMemory(capacity=10)
self.system_prompt = "You are a foraging agent..."
def decide(self, observation, tools) -> AgentDecision:
self.memory.store(observation)
prompt = self.build_prompt(observation)
response = self.backend.generate_with_tools(prompt, tools)
return AgentDecision.from_response(response)
You decide what goes in the prompt, how memory works, and how responses are processed.
Best for: Once you understand the basics. Learning prompt engineering and memory management.
Layer 3: Advanced (Full Control)
You implement everything: custom memory systems, planning algorithms, multi-step reasoning:
class MyAgent(AgentBehavior):
def __init__(self, backend):
self.backend = backend
self.memory = MyCustomRAGMemory()
self.planner = HierarchicalPlanner()
self.world_model = WorldStateTracker()
def decide(self, observation, tools) -> AgentDecision:
self.world_model.update(observation)
plan = self.planner.generate_plan(self.world_model.state)
# ... full control over everything
Best for: Building sophisticated agents. Implementing research ideas.
Growing with the Layers
You don't have to stay at one layer. Start simple, then peel back abstractions as you learn:
- Build a simple foraging agent at Layer 1
- Hit limitations (memory, planning)
- Drop to Layer 2, implement memory yourself
- Learn why certain memory strategies work
- Build an advanced agent at Layer 3 for complex scenarios
The framework peels back as your skills increase.
Agent Literacy, Not Just Agents
Agent Arena isn't about building agents that win scenarios. It's about building understanding of how agents work.
Learning Where Agents Fail
Success teaches less than failure. When your agent fails a scenario, you learn:
- What situations confuse agents
- Where planning breaks down
- When memory helps and when it hurts
- Why certain architectures struggle with certain tasks
This knowledge of failure modes is what separates someone who can copy agent patterns from someone who can design agent systems.
Durable Understanding
Prompt tricks come and go. Frameworks change. LLMs improve.
But the fundamentals — perception, tool use, memory, planning, debugging — are durable. They apply regardless of which LLM you use or which framework is popular this year.
Agent Arena teaches fundamentals through practice, giving you skills that last.
What Comes Next
Agent Arena is just beginning. Here's what we're building:
More Scenarios
Additional benchmark scenarios with explicit learning goals, covering more agentic skills and edge cases.
Reflection and Self-Improvement
Hooks for agents to reflect between runs and systematically improve their behavior.
Comparative Tools
A/B testing for agent implementations. Run two versions against the same scenario and see which performs better — with statistical significance.
Community Sharing
Share agents, scenarios, and insights. Learn from others' approaches. See how different architectures perform on standardized benchmarks.
Growing Curriculum
As the community discovers new failure modes and teaching opportunities, the curriculum will expand. Agent Arena is a living educational platform.
Start Learning
Agent Arena is designed for one purpose: helping you understand how agentic AI actually works.
Not through tutorials that stop at toy demos. Not through frameworks that hide what's happening. Through hands-on experience building agents, watching them fail, understanding why, and improving them systematically.
The scenarios are waiting. The tools are visible. The simulations are deterministic. Everything you need to learn is inspectable.
The only thing left is to start building.
This concludes our six-part series on Agent Arena. We've covered why we built it, what skills you'll learn, the core learning loop, why we chose a game engine, how tools, memory, and debugging work, and now the scenario-based curriculum.
Ready to build your first agent? Check out the Agent Arena product page or contact us for early access.
Agent Arena is an open-source project from JustInternetAI, founded by Andrew Madison and Justin Madison.