Context-Compress Pattern
Conceptual Architecture: Semantic sliding-window and state-ledger engine for long-horizon agent runs
Context & Core Problem
In long-running autonomous workflows (100+ steps), raw transcripts exceed optimal context windows, causing prompt bloat, skyrocketing costs, and severe attention degradation.
Architectural Approach & Decisions
Designed a dual-tier memory system: an immutable structured Fact Ledger (containing key-value state, created files, and verified truths) coupled with a compressed summary of intermediate turns.
Verifiable Evidence & Observable Outcomes
Design Specification: Dual-tier memory maintains immutable entity anchors in an append-only ledger while keeping active prompt windows bounded.
Empirical Result
Structural pattern ensuring critical parameters and file paths survive context compaction without relying on stochastic re-summarization.
System Architecture & Data Flow
Turn Stream
Monitors active token usage against threshold (70% window)
Entity Extractor
Pulls verified facts, paths, and constraints into ledger
Compactor
Replaces verbose reasoning traces with compact state pointers
Re-hydration
Injects relevant ledger facts on demand into subagent prompt
Technical Architecture Specifications
Transparent Limitations & Governance Boundaries
Conceptual reference architecture illustrating memory topology; not a commissioned client engagement.