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Implement Agent Logic with OpenAI o3 and Simulation Environment

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Linked challenge: Multi-Agent AI for Smart Infrastructure Bidding & Risk (AutoGen, OpenAI o3, Ray Tune)

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Linked challenge
Multi-Agent AI for Smart Infrastructure Bidding & Risk (AutoGen, OpenAI o3, Ray Tune)

Prompt source

Original prompt text with formatting preserved for inspection.

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Develop the core logic for each agent using OpenAI o3 (via API) to interpret project briefs, market data (e.g., current construction material prices, labor costs), and competitor profiles. Create a basic Python simulation environment where these agents can submit bids and outcomes are determined based on predefined criteria.

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.