Your AI Cache Is Confidently Wrong — Here's How We're Fixing It

Nagaraju Nampally — Tue, 16 Jun 2026 14:52:34 +0000

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

The memory layer for your agent

Nagaraju Nampally — Mon, 15 Jun 2026 20:28:35 +0000

What started as a weekend side project over a beer turned into something much bigger than expected.

About a month ago, started building Memzent AI for fun — an intelligent semantic proxy that sits between AI agents and LLMs. The original idea was simple:

"Don't pay for the same answer twice."

Use semantic caching to recognize similar prompts and return previously generated responses instead of calling an expensive model again.

Then hit "the" problem.

Consider these two prompts:

→ Transfer $100 from account 123 to account 456
→ Transfer $100 from account 456 to account 123

A semantic search engine sees them as nearly identical.

A production system absolutely should not.

That realization led to what became the Evolution Pipeline — a series of deterministic safety and optimization layers that run before an LLM is ever called.

🔬 E1: Entity Extraction
Extracts critical entities with directional awareness (source vs destination, sender vs receiver, etc.).

⚡ E2: L1b Hot Path Cache
Entity-keyed lookups in Valkey for sub-millisecond response times without vector searches.

📊 E3: Offline Learning Plane
Asynchronous telemetry mining designed to be PII-safe and production-friendly.

🔄 E4: Workflow Registry
Automatically discovers recurring workflows and reusable execution patterns.

📈 E5: GPU Avoidance Rate
Our primary metric: how many requests are resolved without touching an LLM.

🧠 E6: Pattern Mining & Pre-Warming
Learns common request sequences and proactively prepares cache paths before they're needed.

The stack today:

Go Gateway
Rust gRPC Router
Qdrant Vector Engine
Valkey Cache
Next.js Dashboard

Built entirely in nights and weekends alongside a full-time job.

The more we worked on it, the more it evolved from "semantic caching" into something closer to an intelligent AI request router — one that understands when an expensive LLM call can be safely avoided.

It's fully open source and still evolving.

I'd genuinely love feedback from engineers working on AI infrastructure, agents, RAG systems, caching layers, gateways, or inference optimization.

What's over-engineered?
What's missing?
What would you build differently?

Star it. Break it. Roast it.

GitHub: GitHub
Docs: https://lnkd.in/gwfc8qXu
Website: https://memzent.ai
Intro blog : https://lnkd.in/gQrqVHRt

Great work Opsylux team - @maninampally Jagan MRP Madhuri Vilasagaram Manoj V Opsylux LLC, Memzent.AI

DEV Community: Nagaraju Nampally

Your AI Cache Is Confidently Wrong — Here's How We're Fixing It

The memory layer for your agent