Every agent system follows the same basic pattern: observe the world, make a decision, take an action, repeat. But that's not enough for learning how agents work.
Agent Arena adds three critical steps that transform "running agents" into "understanding agents": inspect, replay, and improve. Together, these six steps form the core learning loop that builds real intuition for agentic AI.
The Loop
Observe → Decide → Act → Inspect → Replay → Improve
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Let's break down each step.
1. Observe
Every tick, your agent receives an observation from the simulation. This isn't a vague description — it's structured data about the world state:
- What's nearby: Resources, obstacles, hazards, other agents
- Agent state: Position, inventory, health, energy
- Recent events: What happened since the last observation
- Available tools: What actions the agent can take right now
The observation is grounded in the simulation. It reflects reality, not what the agent hopes or imagines. If a resource isn't in the observation, the agent doesn't know about it.
What you learn: How to process structured observations. What information matters for different decisions. How to maintain an accurate world model when observations are partial or noisy.
2. Decide
Given an observation, the agent must choose an action. In Agent Arena, this means selecting a tool and providing parameters.
This is where the LLM comes in (if you're using one). The agent's decision-making process might involve:
- Consulting memory for relevant past experiences
- Evaluating the current goal against the observed state
- Considering available tools and their likely outcomes
- Selecting the best action given all available information
The decision is explicit: which tool, what parameters, and (optionally) the reasoning behind the choice.
What you learn: How to structure prompts that lead to good decisions. How to balance reaction vs. planning. How memory and context affect decision quality.
3. Act
The chosen action executes in the simulation. This is deterministic — given the same world state and the same action, the outcome is always identical.
Actions have real consequences:
- Movement changes position
- Collection adds to inventory
- Failed actions waste time
- Some actions are irreversible
The agent doesn't just "say" it's moving — it actually moves, and the world state changes.
What you learn: How tool execution works in practice. What happens when actions fail. The gap between intending an action and successfully executing it.
4. Inspect
Here's where Agent Arena diverges from typical agent frameworks. After each action (or at any point), you can inspect exactly what happened:
- Observation received: The raw data the agent saw
- Prompt sent: If using an LLM, the exact prompt including system prompt, memory, and observation
- Decision made: The tool selected, parameters provided, and any reasoning
- Action result: What actually happened in the simulation
- State changes: How the world changed as a result
Nothing is hidden. You can trace any decision back to its inputs and understand exactly why the agent did what it did.
What you learn: Why agents make the decisions they make. Where failures originate — bad observation processing? Bad reasoning? Bad tool selection? Bad parameters? You can tell.
5. Replay
Every simulation run is deterministic and logged. That means you can replay any run exactly:
- Same initial conditions
- Same random seed
- Same sequence of observations and actions
- Same outcomes
This isn't just convenient — it's essential for debugging. When your agent does something weird, you can replay that exact moment as many times as you need to understand it.
You can also:
- Step through tick by tick
- Pause at any point and inspect state
- Compare different runs side by side
- Share runs with others for collaborative debugging
What you learn: How to debug systematically instead of guessing. How to reproduce problems reliably. How to isolate the exact moment where things go wrong.
6. Improve
The final step: making your agent better based on what you learned.
This isn't automatic. Agent Arena doesn't magically improve your agent. But it gives you the information you need to improve it yourself:
- Identify failure patterns through inspection
- Understand root causes through replay
- Modify prompts, memory strategies, or tool selection logic
- Run again and see if behavior improves
For more advanced use, agents can also reflect programmatically between runs — evaluating their own performance and adjusting approach. But this is explicit, not magical. You implement the reflection logic; you control what the agent "learns."
What you learn: How to systematically improve agent behavior. How to distinguish between "this prompt change helped" and "I got lucky." How to build agents that get better over time.
Why This Loop Matters
Most people who "learn" agentic AI stop at step 3. They run an agent, see it succeed or fail, and move on. Maybe they tweak the prompt if it failed.
That's not learning. That's trial and error with no understanding.
The inspect-replay-improve steps are what transform running agents into understanding agents. They're what build the intuition you need to design good agents from the start, not just fix broken ones through random changes.
The Loop in Practice
Here's what the learning loop looks like in a real Agent Arena session:
Run 1: Your foraging agent collects some resources but misses an obvious cluster nearby.
Inspect: You look at the observation it received. The cluster was visible. You check the prompt — the observation was formatted correctly. You look at the decision — the agent chose to move toward a different, smaller cluster.
Hypothesis: The agent is prioritizing the nearest resource, not the best cluster.
Replay: You step through the decision moment. Yes, the agent's reasoning mentioned "closest resource." It didn't evaluate cluster size.
Improve: You modify the prompt to emphasize evaluating all visible resources, not just the nearest one.
Run 2: The agent now evaluates clusters but sometimes backtracks inefficiently.
Inspect: The agent is reconsidering its target each tick, leading to oscillation between two clusters.
Improve: You add short-term memory so the agent commits to a target until it's reached.
Run 3: Much better. The agent commits to targets and collects efficiently.
This is learning. Not "my agent works now" but "I understand how observation processing, prompt design, and memory interact to produce behavior."
Determinism Enables Learning
None of this works without determinism. If every run is different — if there's uncontrolled randomness, race conditions, or non-reproducible behavior — you can't isolate variables. You can't tell if a change helped or if you just got a different roll of the dice.
Agent Arena's simulation is fully deterministic:
- Fixed tick rate
- Seedable randomness
- Ordered event processing
- Reproducible execution
When you change something and behavior changes, you know the change caused it.
Building Intuition
The goal of the learning loop isn't to produce one working agent. It's to build your intuition for how agents work so you can design good agents from scratch.
After running through this loop many times across different scenarios, you start to develop instincts:
- "This observation format will confuse the agent"
- "This goal requires multi-step planning, not reactive behavior"
- "I need memory here to avoid oscillation"
- "This failure pattern suggests the agent can't recover from unexpected states"
These instincts are what separate developers who can build agents from developers who just run them.
Try It Yourself
The loop sounds simple — and it is, conceptually. The challenge is doing it with real agents, real scenarios, and real failures.
That's what Agent Arena provides: a place to run this loop as many times as you need, with full visibility and reproducibility, until you understand how agents really work.
This is Part 3 of our series on Agent Arena. Next up: Why We Chose a Game Engine — the technical architecture that makes the learning loop possible.
Previous posts: Why We Built Agent Arena | What Agentic AI Skills Actually Mean