What Doom taught us about AI-assisted incident response | Rootly
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What Doom taught us about AI-assisted incident response
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What Doom taught us about AI-assisted incident response<br>Sylvain Kalache<br>July 16, 2026
Introducing Doom Agent Arena, an open-source real-time game environment benchmark where AI agents control Doom players via MCP, and fight each other across multiple rounds.<br>By building Doom Agent Arena, our goal was to expand our research scope to better understand how LLMs can assist incident responders in resolving outages as quickly and effectively as possible. Although built in a game setting, the benchmark targets reliability-relevant LLM skills: interpreting dynamic environments, reasoning about consequences, recovering from failed plans, and adapting under time or cost constraints.<br>Let’s dive into how we built it, the methodology, and the findings.
How Doom Agent Arena was built<br>Most other Doom benchmarks feed game frames to a vision model. But because our goal was to test LLMs' reasoning capabilities, our benchmark is instead letting agents observe the Doom game state via our MCP server as structured JSON, including the map and resources, and submit high-level plans for Doom to execute.<br>Latency was one of the main engineering challenges. Doom is built for humans, who make decisions in milliseconds, but a model round-trip takes far longer, so we had to balance the two. The tool calls themselves are fast, but the model's thinking takes up to 8 seconds per decision. That ruled out letting the model play in real time. So we went with a mixed approach: the model makes the high-level decisions, like what the player should do and where it should go, while Doom's engine handles the execution (moving, aiming, shooting).<br>Methodology<br>The game is a one-on-one Doom deathmatch on a walled arena stocked with resources: health packs and a shotgun. Two AI agents each control a player, and the goal is simple: kill the opponent before they kill you. Agents have to read the map, find routes around the walls, grab the shotgun and health at the right moments, and decide when to push a fight and when to retreat to heal.
To keep it fair, we built a custom symmetrical map so neither side starts with an edge. For the findings we will present, we ran four of OpenAI's models against each other across 120 matches, 20 rounds per pairing, swapping spawns at the halfway mark.<br>Findings<br>Model gpt-5.5 finished first at a 66%...