"Agentic AI" has become a buzzword. Everyone's building agents, but few people can articulate what that actually requires. It's not just prompt engineering with extra steps.
Building agents that work — really work, not just demo well — requires a specific set of skills that most tutorials never teach. These aren't abstract concepts. They're practical capabilities you need to develop through hands-on experience.
Here's what Agent Arena is designed to teach, and why each skill matters.
1. Turning Observations into Context
Agents don't see the world directly. They receive observations — structured data about their environment, the results of their previous actions, feedback from tools they've called.
The skill: Taking raw, potentially noisy observations and transforming them into structured, actionable context that informs good decisions.
Why it matters: In production systems, observations come from APIs, databases, user inputs, and sensors. They're messy. An agent that can't process observations effectively will make decisions based on garbage — and produce garbage.
What goes wrong without it: Agents that ignore relevant information. Agents that hallucinate details that weren't in the observation. Agents that get confused by formatting changes. Agents that can't distinguish between "I don't see X" and "X doesn't exist."
In Agent Arena, agents receive observations from a simulated world. You learn to parse what matters, ignore what doesn't, and maintain an accurate mental model of your environment.
2. Choosing Actions via Tools
Real agents don't generate prose and hope something happens. They select from a defined set of tools — explicit, schema-validated actions with clear inputs and outputs.
The skill: Designing tool interfaces, selecting the right tool for each situation, handling tool failures gracefully, and chaining tools together to accomplish complex goals.
Why it matters: Every production agent system uses tool calling. Whether it's function calling in GPT, tool use in Claude, or custom integrations — agents act through tools. Understanding tool design is non-negotiable.
What goes wrong without it: Agents that try to "imagine" actions instead of calling tools. Agents that call tools with malformed parameters. Agents that don't know what to do when a tool fails. Agents that can't decompose complex actions into tool sequences.
In Agent Arena, tools are explicit and their execution is visible. You see exactly what your agent tried to do, what parameters it passed, and what happened. No magic.
3. Managing Memory Across Time
Agents that operate over multiple steps need memory. But memory is hard. Too little and the agent forgets important context. Too much and the agent drowns in irrelevant history, wasting tokens and making worse decisions.
The skill: Implementing bounded short-term memory, retrieval-based long-term memory, and knowing what to remember versus what to forget.
Why it matters: Memory management is where most agent systems break down at scale. An agent that dumps everything into context will hit token limits, slow down, and lose coherence. An agent with no memory will repeat mistakes and lose track of goals.
What goes wrong without it: Context windows stuffed with irrelevant history. Agents that forget what they were doing. Agents that repeat the same failed action because they don't remember trying it. Agents that can't maintain state across interactions.
In Agent Arena, memory is explicit and inspectable. You can see what your agent remembers, why it retrieved certain memories, and how memory affects decisions.
4. Planning Over Multiple Steps
Simple agents react to the current situation. Useful agents plan ahead — breaking down goals into subgoals, sequencing actions, and maintaining a plan across multiple steps.
The skill: Goal decomposition, plan generation, plan execution, and crucially — knowing when to abandon a plan and replan.
Why it matters: Any task more complex than a single action requires planning. Research requires multiple searches, synthesis, and iteration. Coding requires understanding the problem, designing a solution, implementing it, and testing. Without planning, agents flail.
What goes wrong without it: Agents that solve the immediate problem but miss the larger goal. Agents that get stuck in loops. Agents that can't recover from unexpected situations. Agents that don't know when to give up on a failing approach.
In Agent Arena, scenarios require multi-step planning. You can't solve a crafting chain by reacting to each moment — you need to plan your resource gathering, understand dependencies, and execute in order.
5. Replanning When the World Changes
Plans fail. The world doesn't cooperate. Resources you expected aren't there. Actions don't have the effects you predicted. Other agents interfere.
The skill: Detecting when a plan is no longer valid, diagnosing what went wrong, and generating a new plan that accounts for the changed situation.
Why it matters: Static plans are useless in dynamic environments. Production agents face constantly changing conditions — API failures, unexpected user inputs, state changes from other systems. Rigid agents break; adaptive agents succeed.
What goes wrong without it: Agents that keep executing failed plans. Agents that give up at the first obstacle. Agents that can't distinguish between "this plan failed" and "this goal is impossible." Agents that thrash between plans without committing.
In Agent Arena, the simulation is dynamic. Resources move, hazards appear, other agents act. Your agent must learn to adapt.
6. Debugging Agent Failures
Here's the skill nobody talks about: figuring out why your agent broke.
The skill: Tracing agent decisions back to their inputs. Identifying whether failures come from bad observations, bad reasoning, bad tool use, or bad memory. Reproducing problems reliably. Fixing root causes instead of symptoms.
Why it matters: Agents fail. A lot. If you can't debug them, you can't improve them. You'll be stuck tweaking prompts randomly, hoping something works, never understanding why it does or doesn't.
What goes wrong without it: "I don't know why it did that." Prompt changes that fix one problem and cause three others. Inability to reproduce bugs. Agents that work in demos and fail in production.
In Agent Arena, debugging is a first-class concept. Every decision is logged. Every run is replayable. You can step through your agent's execution tick by tick and understand exactly what happened.
7. Improving Across Runs
The final skill: getting better over time. Not just within a single episode, but across multiple runs.
The skill: Reflection, self-evaluation, and systematic improvement. Understanding what worked, what didn't, and why. Applying lessons learned to future behavior.
Why it matters: Agents that can't learn from experience are static. They'll make the same mistakes forever. Agents that reflect and improve get better with use.
What goes wrong without it: Agents that repeat identical failures. No systematic way to improve agent behavior. Reliance on human intervention to fix recurring problems.
In Agent Arena, agents can reflect between runs. You'll build agents that evaluate their own performance and adjust their approach — not through magical "learning," but through explicit, inspectable reflection.
Skills, Not Tricks
Notice what's not on this list: prompt templates, API wrappers, or framework-specific patterns.
These are skills — capabilities you develop through practice, not snippets you copy. They transfer across frameworks, across LLMs, across domains. An agent developer who understands these fundamentals can build effective agents anywhere.
That's what Agent Arena teaches. Not how to use a specific framework, but how agent systems actually work.
Building Intuition
You can't learn these skills by reading about them. You have to build agents that perceive, decide, act, remember, plan, fail, and recover. You have to watch them work and watch them break. You have to debug failures until you understand root causes.
That's why Agent Arena exists: to give you a place to develop these skills through practice, with immediate feedback and full visibility into what's happening.
The skills are hard. But they're learnable. And once you have them, you can build agents that actually work.
This is Part 2 of our series on Agent Arena. Next up: The Core Learning Loop — how Agent Arena's observe-decide-act-inspect-replay-improve cycle builds real agent intuition.
Missed the introduction? Start with Why We Built Agent Arena.