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AutoGen Adversarial Initialization

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Linked challenge: Adversarial Strategy Simulator with AutoGen and GPT-5.4 Pro

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Linked challenge

Adversarial Strategy Simulator with AutoGen and GPT-5.4 Pro

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Original prompt text with formatting preserved for inspection.

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Setup an AutoGen environment with two ConversableAgents. Agent A uses GPT-5.4 Pro and is instructed to play perfectly. Agent B uses Claude Sonnet 4.6.6 and is instructed to find creative, sub-optimal moves to win. Set the termination condition to 10 turns.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.

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implementation

This prompt is mostly narrative and instruction-driven, so adapt examples and output constraints before you rewrite the structure.

Linked challenge

Adversarial Strategy Simulator with AutoGen and GPT-5.4 Pro

Taking inspiration from chess grandmasters who are developing sub-optimal human strategies to beat perfect AI play, this challenge focuses on building an adversarial simulation engine. You will use AutoGen to orchestrate a game-theoretic battle between GPT-5.4 Pro (acting as the Perfect Engine) and Claude Sonnet 4.6.6 (acting as the Human Strategist). The goal is to evolve non-obvious strategies that exploit the deterministic nature of high-end models. The environment will be served via Seldon Core for scalable inference, and Replicate will be used to host smaller, specialized models for specific tactic generation. This project explores the frontiers of AI-to-AI interaction where the objective is not just optimization, but creative deviation from the norm.

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