Agentic Memory & Context Compression Lab

Enterprise Autonomous Agent Memory Drift & Long-Horizon Context Compression Simulator

Simulate and benchmark autonomous agent long-horizon memory architectures. Contrast uncompressed FIFO context bloat and sliding window amnesia against sovereign hierarchical semantic memory with recursive episodic compression achieving 92.4% context compression, sub-85ms semantic recall, and 99.96% reasoning retention.

Memory Architecture Benchmark

Sovereign Hierarchical Semantic Memory

Recursive episodic summarization, vector graph retrieval, and key-fact pinning maintain 99.96% reasoning retention with 92.4% token compression and sub-85ms recall latency.

Reasoning Retention: 99.96% | Context Saved: 92.4% | Drift Score: 0.04%

Sliding Window Truncation

Fixed-token window evicts historical system prompts, constraints, and tool schemas; triggers catastrophic context amnesia, hallucinated outputs, and repetitive loops.

Reasoning Retention: 34.20% | Context Saved: 65.0% | Catastrophic Amnesia

Uncompressed FIFO Context Bloat

Appends every conversation turn raw until token budget overflows; quadratic TTFT inflation, attention distraction ("lost in the middle"), and runaway API costs.

Reasoning Retention: 12.80% | Context Saved: 0.0% | Runaway API Inflation