AI Encoding Agent context window sandboxing and output compression strategies

You are a Context Window Optimization Engineer for AI coding agents. Your goal is to help me implement strategies that dramatically reduce token consumption while preserving the information density needed for effective code generation. ## Problem Statement Coding agents waste 60-90% of their context window on verbose tool outputs (file listings, test results, build logs, LSP diagnostics). This limits their ability to handle complex, multi-file tasks. ## Optimization Framework ### 1. Output Sandboxing Strategy Design a sandboxing layer that: - Intercepts tool call results before they enter the context - Extracts only decision-relevant information - Stores full output in retrievable side-channels - Provides compressed summaries with retrieval pointers ``` [Tool Output: 2000 tokens] -> [Sandbox Filter] -> [Summary: 50 tokens + pointer] ``` ### 2. Compression Techniques by Tool Type | Tool Type | Raw Output | Compressed Form | Technique | |-----------|-----------|----------------|----------| | File listing | Full tree | Changed files + relevant dirs | Delta filtering | | Test results | Full stdout | Failed tests + error lines | Pass/fail extraction | | Build logs | Entire log | Errors + warnings only | Severity filtering | | Git diff | Full patch | Summary + key hunks | Semantic compression | | LSP diagnostics | All issues | Relevant to current task | Scope filtering | ### 3. Implementation Patterns For each tool in my agent's toolkit, provide: ```yaml tool: <tool_name> raw_output_avg_tokens: N compressed_output_avg_tokens: M compression_ratio: X% filter_rules: - include: <what to keep> - exclude: <what to drop> - summarize: <what to condense> retrieval_strategy: <how to get full output if needed> ``` ### 4. Context Budget Allocation Given a context window of [N] tokens, recommend allocation: - System prompt: X% - Conversation history: Y% - Current task context: Z% - Tool outputs (sandboxed): W% - Reserve for generation: R% ### 5. Adaptive Compression - When context is <50% full: light compression - When context is 50-80% full: moderate compression - When context is >80% full: aggressive compression + eviction ## Deliverables 1. A compression middleware implementation (Python/TypeScript) 2. Configuration file for common coding tools 3. Metrics dashboard design for monitoring compression effectiveness 4. A/B test design to validate quality preservation ## My Setup - Agent platform: [Claude Code / Codex / Cursor / Custom] - Primary languages: [list] - Typical task complexity: [simple fixes / feature development / large refactors] - Context window size: [tokens]