Feature: Implement intelligent context-pruning for LLM retries

[pradeep0153]

Closes #244.

Description

This PR executes a major performance and cost-optimization initiative for the core agent loop. During an agent loop, if the initial LLM response failed the test suite, the agent fed the error back to the LLM via retry_request().

However, context_builder.py mathematically continued to send the exact same massive 60K-character XML context on every retry. Since the LLM already inherently understands the repository context from the first prompt in the conversation thread, appending the full file tree to iterations 2, 3, and 4 drastically inflated token usage, slowed down response times, and skyrocketed API costs without providing any new value.

Changes Made

• Context Builder Refactor: Implemented a state-aware context-pruning strategy within LLMClient.retry_request() and context_builder.py.
• Differential Payload: On iterations > 1, the context builder is now mathematically constrained. It only includes files that were actually modified in the previous step, plus the raw error output from the sandbox.
• Omission Strategy: The full, unaltered file tree is dynamically omitted, relying securely on the LLM's conversation history to maintain broader project context.

Impact

• API Cost & Latency: Blindly re-sending 15,000 tokens of static context on a 5-iteration debug loop is a fatal architectural inefficiency. By engineering this context-pruning pipeline, we mathematically slash token usage. We drastically accelerate the LLM's time-to-first-byte (TTFB) and cut API costs by up to 70% for long debugging loops, ensuring the agent remains economically viable at scale.

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