Agent Memory is an Infrastructure Problem

Today's AI agents are stateless. They forget everything between sessions. Context doesn't survive restarts. This isn't a model limitation—it's an infrastructure gap.

The Problem

Current approaches treat memory as an application-layer concern. Developers implement ad-hoc solutions: JSON files, SQLite databases, vector stores with no integrity guarantees. This works for demos. It fails in production.

What Goes Wrong

Memory Corruption: No verification layer. Poisoned data propagates silently. By the time you detect it, the damage is done.

Context Loss: Conversation history disappears. Learned patterns evaporate. Agents can't build on past knowledge.

No Auditability: Regulators demand audit trails. Compliance requires tamper-proof records. Your agent has neither.

The Infrastructure Layer

What we need:

Durable Storage: SHA-256 signed artifacts. Immutable audit trails. Git-like versioning for knowledge graphs.

Semantic Search: Not keyword matching—semantic embeddings. Context-aware retrieval. JSON-LD semantic web standards.

Integrity Verification: Real-time detection of corrupted data. Automatic rollback to last known-good state. Forensic timeline reconstruction.

Why It Matters

Research Assistants: Need complete literature memory. Can't lose citations or learned hypotheses.

Enterprise Agents: Require compliance-ready audit trails. Must survive system failures.

Autonomous Trading: Years of market memory. Can't tolerate data loss or corruption.

The Path Forward

Agent memory is security-critical, compliance-required, and commercially valuable. It deserves infrastructure-grade tooling.

That's what we're building at Novyx Labs.