AWS AI League 2026: NYC Summit - A Winning Walkthrough
How I rebuilt my agent between re:Invent and the New York Summit and won the AWS AI League, plus the agentic launches and the robotics work that stole the show.
Back in June, everyone in NYC was talking about the championship. Not the one at the Garden. The one happening a few blocks away at the Javits Center, where a room full of builders spent the day teaching AI agents to crawl through dungeons, dodge traps, and outscore each other for a spot on the leaderboard. That is the AWS AI League, and at the AWS Summit in New York I walked out of it in first place.
The Javits Center, and the way in. A few blocks from the Garden, and a very different kind of championship.
Now that the dust has settled and I have had a month or so to sit with it, I wanted to write the full story.
The road to New York
If you have read my 2025 re:Invent recap , you know the backstory. I came up through AWS DeepRacer, made the jump to the AI League, and entered the 2025 championship as a wildcard because I could not make it to any of the qualifying Summits. With about five minutes left before the submission deadline, one last upload pushed my username into a qualifying spot and onto the championship stage at re:Invent. I finished as the global 1st runner-up that year, second in the world.
Second is a great result. It is also the kind of result that keeps you up at night thinking about the one round that could have gone differently. So when the AI League came to the New York Summit, I showed up with a year of lessons, a rebuilt agent, and exactly one goal.
What the AWS AI League actually is
Here is the part that makes this competition so much fun to talk about. The AWS AI League is a roguelike dungeon crawler where the hero is an AI agent that you design and build.
Your agent spawns on a grid map and has to find its way to the treasure. Between the start and the goal is a maze full of stuff: walls to route around, spike traps that cost health, coins that boost your score, and locked doors that block the only path forward. Scattered across the map are challenge tiles, and every time your agent steps onto one it has to solve a live problem on the spot. Write and run code to crack a number too big for a language model to compute in its head. Scrape a website and pull out one exact fact. Recall something it was told earlier from memory. Get a guardrail challenge right, which comes down to being deliberate about exactly which words you have told it to block. Find a key, hold onto the code, and transform it correctly to open the matching door later.
A full map in the workshop. Two keys, two locked doors, coins, spike traps, monsters and the treasure, with five lives and five minutes on the clock. The panel on the right is where you guide the pathfinding tool.
Four things are scored at once: the points you earn from challenges, the lives you keep, how fast you finish, and how few tokens your agent spends getting there. The interesting part is that these pull against each other, and the real game is deciding which ones to chase.
That decision is the route. Do you send your agent after every challenge tile on the board, banking the points but spending lives on traps and burning clock along the way? Or do you pick off the high value, low risk challenges and drive hard for the treasure? There is no single right answer, and it changes map by map depending on where the keys sit, how many lives a detour costs, and how much time the clock gives you. Route strategy is the biggest lever in the whole competition and it is where I spend most of my thinking.
Token efficiency is real, but it is the last few percent. It only starts to decide anything once you are already fighting for the top few places on the board. Get the route wrong and no amount of concise output will save you.
It is genuinely a different kind of competition. You are not writing one program. You are building an autonomous system, handing it the controls, and trusting it to make good decisions in situations you did not get to see in advance.
New for this season
Three things were new this time, and all three changed how you build.
Keys and locked doors. This is a whole new paradigm for pathfinding. It stops being "find the shortest route" and becomes an ordering problem with dependencies. Your agent has to reach the right key first, hold onto the code, transform it correctly, and spend it on the matching door before that part of the map opens up at all. The highest scoring route is very often not the shortest one.
It is also worth saying plainly that agents are not good at pathfinding on their own. Hand a language model a grid with gated dependencies and ask it to plan a route, and it will give you something confident, plausible, and wrong. That is the whole reason the pathfinding tool matters so much, and why mine ended up growing into a real optimization engine rather than a prompt.
A healthcare challenge. The Risk Analyzer, worth 850 points. Your agent gets a patient's lipid panel written the way a clinician actually writes it, a paragraph of notes with the numbers buried in prose, and has to work out which value is which, score each one against a fixed rubric, total it, classify the result, and return strict JSON. None of it is conceptually hard. It is fiddly, and fiddly is exactly where a generalist orchestrator drops a step. It has to pull eight separate fields out of sentences like "his HDL is only 33", get every threshold and the arithmetic exactly right, and work out that "despite being on metoprolol" means the blood pressure medication box is ticked. That is a job for a narrow specialist with one instruction, not for the agent that is also trying to run the maze.
Fine-tuning folded into the agentic challenge. Model customization used to be its own separate contest. This season the small models you train plug straight into your agent as subagents, which means the architecture you pick for reliability is also the one that opens up the extra points.
Competition day
The format had two acts. First, the workshop round, where everyone iterates on their agent and submits against the maps to climb a live leaderboard. It was led by Seyha Kry and Dana Bullukian, who created the agentic challenge. I finished that round in first place, which earned a seat in the final showdown.
Room 1E15, session AIM401. The workshop round, with everyone in the room iterating against a live leaderboard.
One thing worth saying to anyone on the fence: the workshop is worth doing even if you have no interest in competing. You walk out of it having actually built a working agent with subagents, memory, Lambda tools and guardrails, and applied it to a real problem. The leaderboard is optional. The skills are not.
The workshop leaderboard when submissions closed. Note the token columns on the right, which most people never look at.
The room was full of familiar faces. A good number of former DeepRacer competitors have made the same jump across to the AI League, and sitting next to me for the workshop round was Sai Ram Naragoni, the 2021 DeepRacer world champion.
With Sai Ram Naragoni, who competes as JPMC-Rogue. He finished runner up in New York, then went on to win the DC Summit and take his own spot at re:Invent.
Then the showdown. The top finishers go head to head across a final set of maps that decide the championship, and the maps are not the ones you practiced on. Same rules, new questions, new layout, no second chances.
There is one more twist in the final, and it is the part I did not expect to matter as much as it did. Before each map runs you get one minute at the keyboard to write a prompt for your orchestrator. That is your only chance to tell it anything about the board it is about to walk into and to spell out the strategy you want it to follow. So you are staring at a map you have never seen, working out on the spot whether this is a run where you sweep the challenges or a run where you take the safe points and drive for the treasure, and then getting that decision into words before the clock runs out. Everything I said earlier about route strategy comes down to that minute.
The other thing about the final is that everyone starts level. The workshop scores do not carry over, so the board goes back to zero and the three of you run the same maps side by side. The agent you qualified with is the agent that runs, and that one minute of prompting is the only lever you have left.
Finalists getting set up before the last round, with Seyha and crew keeping an eye on things. That belt was the whole point.
This is where all the unglamorous work pays off, the robustness you built when nobody was watching, the edge cases you handled, the routes you tuned. My agent held up across the final three maps and came out on top.
Final standings at the New York Summit:
- rosscomp1 (me)
- JPMC-Rogue (Sai Ram Naragoni)
- yichao
All three of us walked away with $1,000 in AWS credits.
Round 3, the last map of the showdown. Bigger grid, two colors of locked door, a boss, and 2:30 on the clock.
The AWS crew who put the whole competition on. None of this happens without them.
AgentCore and the agentic advances from AWS
The AI League stand out on the expo floor, running all day alongside the sessions.
Part of what made this Summit special is that the tools I was racing with were the same ones taking center stage on the keynote screen. A few launches stood out.
- Web Search on Amazon Bedrock AgentCore. A fully managed web search tool that lets agents ground their answers in current, cited web knowledge without data leaving your secured AWS environment. If you have ever hand-built a scraping tool for an agent, you understand exactly how useful this is.
- The optimization loop for agents in production. Recommendations, batch evaluations, and A/B tests went generally available, closing the observe, evaluate, and improve loop. This is the part of agent building that usually lives in spreadsheets and guesswork, now turned into a managed workflow.
- Stronger controls as agents grow. Expanded capabilities for enforcing controls that scale as agents take on more. Anyone who has fought with an overeager guardrail will appreciate the direction.
- Kiro for iOS and Amazon Quick's autonomous agents. Kiro went mobile in a gated preview so you can manage sessions from your phone, and Amazon Quick picked up autonomous agents for background work alongside a prioritized activity feed. A Summit that was agentic from top to bottom.
Just before the Summit, AgentCore had also picked up Payments in preview, built with Coinbase and Stripe, letting agents autonomously pay for APIs, MCP servers, paywalled content, and even other agents, with settlement running on stablecoins over Coinbase's x402 protocol. Agents that do not just decide but transact.
Spending the morning building a competition agent on AgentCore and then watching AWS announce GA-level capabilities for the exact same primitives at the same event is a strange and great feeling. It tells you the thing you stayed up late tinkering with is the thing the industry is actually about to use.
Extra Credit: Where LLMs and robotics meet
This is the thread I am most personally excited about, so bear with me while I geek out. One of the best parts of any Summit is who you bump into on the floor.
I walked up to this particular booth because a Strands logo sitting next to a table full of robots was not something I was going to walk past. Then I spotted the little four wheeled car among them, and anyone who came up through DeepRacer will know exactly why that stopped me. I asked one of the people running the booth what it was, and they waved Cagatay Cali over to come and talk to me.
With Cagatay Cali on the expo floor, in front of the robots.
Cagatay Cali is a Research Engineer at AWS working right at the intersection of agentic AI and robotics. He is a core contributor across the Strands ecosystem, from the Agents SDK itself to Strands Robots in the experimental Strands Labs organization, and his work chases one deceptively simple idea: let a person control a physical robot with plain natural language.
If you have not run into Strands yet, here is the quick version. Strands Agents is AWS's open-source, model-driven SDK for building agents. I have been building with it inside Aqfer to put together our own agentic systems, which is exactly why that logo on the booth pulled me over. Strands Labs is the sandbox where the team pushes the frontier, and Strands Robots is the project that takes everything I was doing in a software dungeon and points it at real hardware. It exposes more than seventy robots across eight categories, and the whole robot lifecycle, simulation, control, training, and deployment, as tools that a single Strands agent can compose. The same code drives a robot in a MuJoCo simulation and on a physical machine with one parameter change, and vision-language-action models like NVIDIA GR00T and the Hugging Face LeRobot policies turn a camera feed and a sentence into motor commands. Small models run at the edge on hardware like NVIDIA Jetson for instant reflexes, while Claude on Amazon Bedrock AgentCore handles the slower, deliberate reasoning. Fast instinct at the edge, careful thought in the cloud.
Strands Agents on the floor, driving real hardware from natural language.
Back to that car. It was an EarthRover Mini+, the compact open-source rover from FrodoBots: a small four-wheeled robot with a camera, the kind of thing you can pick up for a few hundred dollars and drive over 4G from anywhere. In the Strands Robots code it is a first-class citizen, spun up in a couple of lines and driven with W/A/S/D velocity keys through the keyboard_rover teleoperator. Watching that little camera-equipped rover take a natural-language instruction, look at the world, and decide how to move, I could not stop thinking about DeepRacer.
The parallels are almost uncanny. DeepRacer put a 1/18 scale car with a single camera in front of thousands of builders and taught them reinforcement learning by letting them train a driving policy in simulation and then watch it run on a real track. Strands Robots does the same move for the agentic era: a small wheeled robot with a camera, a policy learned from data, simulation-to-reality transfer with one line changed, and a continuous loop of sense, decide, act, and learn. You even drive the thing with WASD to record demonstrations, the same tactile on-ramp that made DeepRacer click for so many of us. Cagatay's stated mission, making robotics accessible to builders at any skill level, is the exact DeepRacer promise carried forward, except the track is now the entire physical world.
This is not abstract for me. At home, my son and I are building a Reachy Mini, the small open-source expressive robot from Hugging Face and Pollen Robotics, and here is the full-circle moment: Reachy Mini is one of the robots Strands Robots already supports. The same agentic stack I used to win a software dungeon is a stack I can point at the little robot sitting on our kitchen table. Teaching an agent to reason and then act, whether the body it controls is a maze avatar, a rover, or a desktop robot my kid gets to name, is quickly becoming the throughline of everything I am into.
If any of that got you curious, here is where to start. strandsagents.com is the home of the SDK, Strands Labs is the experimental organization on GitHub, and Strands Robots is the project itself, for controlling real hardware with natural language. Cagatay also maintains awesome-strands-agents , a curated list of everything happening in the ecosystem.
Want the whole playbook?
That is the story. What I have not covered here is how the agent itself was actually built, and that is the part most people ask me about.
The full version of this post is up on the AWS AI & ML Community Blog:
Your turn
You do not have to be at a Summit to play. The AWS AI League virtual league kicks off soon, run online through AWS Builder Center, and it is another live shot at the exact same prize: the top builders advance to the Grand Finale at re:Invent in Las Vegas to compete for the $50,000 prize pool. Same game, same stakes, no plane ticket required to get started.
- AWS AI & ML Community on Discord. The fastest way in, and where events like the virtual league get announced. Join here.
- The AI League community edition repo. The open-source version, deployable as a stack so you can practice against the real thing before you ever hit a live leaderboard. github.com/aws-ai-community/ai-league-community-edition
- The AWS AI League page. Official overview, rules, prize pool, and how to join. aws.amazon.com/ai/aileague
Winning the Summit earned an all-expenses-paid trip to AWS re:Invent to compete on the championship stage. Last year I left re:Invent as the runner-up. This year I get to go back and try to finish the job.
If you have been curious about agentic AI and you want a fun, competitive, hands-on way to actually learn it, come build an agent and join us. I will see you in the dungeon.
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