Last week we shared how Memzent AI avoids paying twice for the same LLM answer. A community member dropped the exact right challenge:
"The next challenge is usually invalidation. If the repo, policy, or user preference changes, the memory layer has to know when a similar answer is no longer a safe answer."
They're right. And we're solving it.
The Real Problem
A stale cache isn't a performance bug — it's a business liability.
Your refund policy changes from 30 days → 14 days. Your AI keeps telling customers 30 days. For an hour. At scale.
TTL is a blunt instrument. Short TTLs kill savings. Long TTLs create risk. Neither is intelligent.
What We're Building (Publicly)
- Event-Driven Invalidation
MCP tools already know when data changes. A GitHub connector knows when code is pushed. A CRM connector knows when docs update.
Tool data change → Event signal → Bust related cache entries → Zero staleness
No TTL guessing. Real-time correctness.
- Version-Tagged Cache Keys
cache_key = hash(prompt + org_id + model + config_version)
Admin updates a policy? config_version bumps. Old cache entries become unreachable instantly.
- Preference Drift Detection
User context evolves mid-session. If their preference fingerprint drifts beyond a threshold — semantic match becomes a cache miss.
The Metric
We don't just track GPU Avoidance Rate. We track Safe Avoidance Rate — responses that were both cached and correct.
Full Deep-Dive
Read the full technical breakdown: https://memzent.ai/blog/semantic-invalidation-when-your-cache-is-wrong
Tracked openly: GitHub Issue #11
We're building Memzent AI in public — an intelligent semantic proxy that sits between AI agents and LLMs. Entity-aware caching, multi-LLM routing, RBAC, and now intelligent invalidation.
Would love feedback from anyone building in this space. What invalidation strategies have worked for you?
⭐ GitHub | 🌐 https://memzent.ai
