HarnessOS: scaffold/middleware for infinite autonomous tasks — built on Harness Engineering

There's a concept gaining traction in AI systems engineering: Harness Engineering. Not the testing tool. The idea: raw LLM capability is like raw power — high voltage, hard to control, dangerous to run indefinitely. Harness Engineering is the discipline of building the control structures that make that power usable at scale. Context managers. Evaluation loops. Failure classifiers. Goal trackers. Memory tiers. I think it's going to be one of the defining disciplines of serious AI systems work. And I've been building a platform around it. What I Built HarnessOS is a scaffold/middleware system for running infinite autonomous tasks. The key word is infinite. Not one task. Not one session. An agent that: Runs continuously, across context window rotations Evolves its own goals when it succeeds at the current one Persists state across sessions without losing context Classifies its own failures and routes them appropriately This is the architecture: HarnessOS ├── CTX ← context precision layer