fix: better handling of params and custom params for optimization

[andrewklatzke]

Requirements

• I have added test coverage for new or changed functionality
• I have followed the repository's pull request submission guidelines
• I have validated my changes against all supported platform versions

Describe the solution you've provided

Implements better handling for params on the initial variation (folds model changes in as overwrites rather than completely replacing) and ensures custom params are persisted unchanged. Additionally makes sure that tools cannot be changed by the optimization process.

Describe alternatives you've considered

This is the result of a bug report so there weren't really alternatives considered.

Additional context

Initially it was assumed that the optimization process would properly pull params forward (via the LLM) but this doesn't seem to always be the case. In the case of custom params, they aren't fed into the LLM calls since they're user-specified data (not specific to the actual optimization result). We now just pull these through as-is. In the case of tools, the model will be able to optimize the prompt to call a specific tool if multiple are provided, but we don't want to strip any tool information from the final result as it may be necessary for the calls to function.

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Note

Medium Risk
Moderate risk because it changes how model parameters and tools are carried forward across optimization iterations and what gets auto-committed, which can affect runtime agent behavior if merging/restoration logic is wrong.

Overview
Improves variation-application logic so LLM-generated current_parameters are merged into existing parameters instead of replacing them, preserving user-specified/custom settings (e.g. max_tokens, response_format) when the LLM omits them.

Prevents tool drift by always restoring the original tools list (and logging when the LLM returns a different one) to avoid silently dropping user tools or leaking internal framework tools.

Captures model.custom from the initial LaunchDarkly variation and includes it when auto-committing a winning variation; adds focused test coverage for parameter persistence, tool restoration/warnings, and model.custom propagation.

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