The problem I am solving for is that AI systems (vector search, RAG pipelines, agent frameworks) don’t usually fail cleanly — they degrade silently, oscillate under load, or keep returning corrupted results until something crashes. Monitoring tells you after the fact; circuit breakers tend to be static and blind to context.
Interlock tries to address that by:
forecasting time-to-failure under stress
intervening before hard limits are reached
refusing to serve results when confidence collapses
producing cryptographically signed evidence of what happened (control vs protected runs)
It includes:
integrations with FAISS, Pinecone, Weaviate, Milvus, LangChain, LlamaIndex (Elasticsearch experimental)
TypeScript + Python support
automated stress tests (control vs protected)
long-run stability tests
certification classes (I–V) derived from actual configuration + behavior, not labels
Importantly: Interlock does not guarantee correctness or uptime. It certifies that a given configuration survived a defined stress test without crashing, oscillating, or serving degraded results — similar to a structural load rating rather than a promise.
The repo is fully open source, and all claims link to test artifacts and CI runs. I’m especially interested in feedback on:
failure modes this wouldn’t catch
where the certification model is too strict or too weak
whether this is actually useful in real production AI systems
Repo: https://github.com/CULPRITCHAOS/Interlock
Happy to answer questions or be told why this is a bad idea lol