Build an LLM-driven text-game agent

Q: Build an LLM-driven text-game agent

This question evaluates a candidate's understanding of integrating large language models into interactive agents, including prompt design, game-state summarization and serialization, constrained action parsing, retry and fallback strategies, context-window management, logging and experiment tracing, testing, and debugging of failure modes.

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You are given a terminal-based text game and a several-hundred-line Python baseline that uses hand-written heuristic rules to choose actions. Replace the heuristic agent with an agent that queries an LLM to decide the next action.

Describe how you would implement this in Python so that the system is robust and debuggable. Your answer should cover:

the interface between the game engine and the LLM agent
how game state should be summarized and serialized into prompts
how to constrain valid actions and parse model outputs safely
retries, fallbacks, and context-window management
logging, tracing, and experiment tracking
how you would test the agent over repeated runs
how you would debug failures such as invalid actions, infinite loops, or hallucinated state

Build an LLM-driven text-game agent

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