> common-context-optimization

Priority: P1 (OPTIMIZATION)

Manage the Attention Budget. Treat context as a scarce resource.

1. Observation Masking (Noise Reduction)

Problem: Large tool outputs (logs, JSON lists) flood context and degrade reasoning. Solution: Replace raw output with semantic summaries after consumption.

1. Identify outputs exceeding 50 lines or 1 KB.
2. Extract critical data points immediately.
3. Mask by rewriting history to replace raw data with a summary placeholder.
4. See references/masking.md for patterns.

See implementation examples for masking patterns.

2. Context Compaction (State Preservation)

Problem: Long conversations drift from original intent. Solution: Recursive summarization that preserves State over Dialogue.

1. Trigger compaction every 10 turns or 8k tokens.
2. Compact:
   • Keep: User Goal, Active Task, Current Errors, Key Decisions.
   • Drop: Chat chit-chat, intermediate tool calls, corrected assumptions.
3. Format: Update System Prompt or Memory File with compacted state.
4. See references/compaction.md for algorithms.

See implementation examples for compacted state format.

3. KV-Cache Awareness (Latency)

Goal: Maximize pre-fill cache hits.

• Static Prefix: Enforce strict ordering — System -> Tools -> RAG -> User.
• Append-Only: Never insert into the middle of history; append new turns only.

References

• Observation Masking Patterns
• Compaction Algorithms

Anti-Patterns

• No raw tool dumps: Mask large outputs immediately after extracting data.
• No unbounded growth: Compact every 10 turns to preserve intent over dialogue.
• No middle insertions: Append-only history maximizes KV cache hits.