Context Preservation vs Summarization

The AI community is obsessed with context window size. 32k tokens! 128k tokens! 1M tokens!

But window size isn't the bottleneck. **Preservation** is.

The Summarization Trap

Most agent frameworks use "memory summarization":

1. Agent completes a conversation 2. Framework summarizes it to 500 tokens 3. Summary gets stored 4. Original context is discarded

This **loses information**. Agents can't learn from what they don't remember.

What Gets Lost

Nuance: Summarization flattens context. "User was frustrated" doesn't capture *why* or *how*.

Citations: Research agents lose the exact quote, page number, publication details.

Decision Logic: Trading agents forget *why* they made a bet—only that they did.

The Argument for Compression

"But storage is expensive! Context windows have limits!"

True. But:

Storage is cheap: 1TB of knowledge graph data costs $0.02/month on S3. Your engineers cost $100/hour.

Semantic search solves retrieval: Don't load everything into context. Query for relevant artifacts.

Versioning enables debugging: Can't debug an agent if you've deleted its memory.

Full-Fidelity Architecture

The right approach:

Store Everything: Persist full conversations, API responses, documents. Disk is cheap.

Index for Search: Semantic embeddings + keyword indexing. Sub-second retrieval.

Load Selectively: Query for relevant context. Only pull what's needed into the window.

Version Everything: Git-like history. Time-travel debugging. Rollback on corruption.

Real-World Benefits

Research Assistants: Search across every paper they've read. Find exact citations.

Customer Support: Recall complete conversation history. No "can you repeat that?"

Autonomous Agents: Learn from cumulative experience. Decisions compound over time.

The Performance Argument

"Won't this be slow?"

No. Semantic search over 1M artifacts takes less than 100ms. Context loading is parallelizable.

Your bottleneck is the LLM call (1-3 seconds), not the memory retrieval (0.1 seconds).

Implementation

What you need:

1. **Durable storage**: SHA-256 verified knowledge graph 2. **Semantic index**: Vector embeddings for similarity search 3. **Version control**: Rollback capability 4. **Query layer**: Fast retrieval of relevant context

This is Novyx Core's architecture.