Body: Most agent systems focus on capability. Very few focus on stability under acceleration. I just open-sourced something I’ve been building: Coherence Stability Kernel (v1.0) MIT licensed. It’s a runtime stability framework for agentic systems that: Monitors five bounded risk signals (normalized 0–1) Aggregates them into a composite risk metric Computes coherence as C = 1 - risk Measures escalation as acceleration vs recovery capacity Enforces regime-based operational limits Core idea: Stability isn’t a prompt problem. It’s a telemetry + regime enforcement problem. Instead of: “Is the output aligned?” It asks: “Is the system accelerating faster than it can recover?” The kernel is deterministic: Replayable behavior Explicit risk normalization Hard regime transitions (normal → elevated → constrained) It’s intentionally: Clinical Governance-oriented Not hype-driven Designed for agent stacks that already exist Repo: https://github.com/noblebrendon-cloud/coherence-stability-kernel� Would appreciate critique on: The composite metric formulation Escalation ratio math Regime enforcement thresholds Failure modes I may be missing This isn’t meant to be flashy. It’s meant to survive pressure. Curious how others are handling runtime stability in autonomous or semi-autonomous systems. submitted by /u/EcstaticAd9869
Originally posted by u/EcstaticAd9869 on r/ArtificialInteligence