Runtime Resilience Framework (RRF): Operational Invariants and Survivability in Long-Running Autonomous AI Agents

Runtime Resilience Framework (RRF) is a systems-engineering methodology for evaluating and stabilizing long-running autonomous AI agents under real-world infrastructure stress. The framework introduces operational invariants, runtime observability metrics, deterministic constraint enforcement layers (RCK), and chaos-based resilience probes (CARP) for survivability-oriented evaluation. Unlike capability-focused benchmarks, RRF focuses on runtime degradation, retry storms, context dilution, resource exhaustion, orchestration collapse, and operational survivability of production-grade AI agents. The work proposes a practical taxonomy of agent failure modes and a resilience-oriented benchmark topology for autonomous execution environments. AAM-V1_ARTSYBASHEV_UA_KHARKIV_AIANALYSIS