Building a Doom-Like World to Explore Agentic Systems - Alexander Chernov - NDC Toronto 2026
This talk presents an architectural framework for building complex agentic systems using a modified Doom-like game engine as a controlled testbed. The core concept is treating agents as 'semantic mirrors' of the game world state, allowing non-player characters (NPCs) to act autonomously while maintaining strict observability and reproducibility. The architecture emphasizes decoupling AI reasoning from the game loop via specialized components like the MCP Gateway, enabling real-world application of simulation techniques in fields such as pharmaceutical R&D.
Key takeaways
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Agentic Systems Architecture
2:00
The system models agents as autonomous entities that perceive the environment and make decisions. The architecture is designed to be observable, attributable, and reproducible through structured world state changes (the 'semantic mirror').
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Two-Tiered Agentic Vision
4:20
To manage latency, a two-tier vision system is implemented: a fast, deterministic observer swarm (7 Hz) for basic tracking, and a slower, LLM-powered tier using 'Lenses' to extract complex semantic information from the environment.
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Architectural Components
5:40
Key components include the Policy Guard system (defining what agents can/cannot do), the MCP Gateway (Model Context Protocol) for external integration, and a Semantic Cache (Mosquito Dog) to reduce latency and cost by caching LLM responses.
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Reproducibility and Validation
7:50
The design ensures determinism through fixed control loops (e.g., 35 ticks per second), state machine transitions, and structured logging of events (JSONL). This allows for full replay and behavioral regression testing.