Autonomous AI agents operating in production environments face a fundamental challenge: the absence of verifiable runtime conformance criteria. While static testing frameworks can validate agent behavior under controlled conditions, they fail to capture the stochastic nature of real-world execution where faults compound, context drifts, and decisions become irreproducible. We introduce the Correctover Conformance Shape (CCS) — a formal framework that defines runtime conformance as the set inclusion Required(τ) ⊆ Supported(τ) for each agent transition τ. This deceptively simple criterion, grounded in empirical analysis of 50,000 production-derived decision traces across 13 LLM providers, reveals a critical gap: single-fault self-healing achieves 97.4% success, while compound fault chains degrade to approximately 72%, exposing 19,251 failure paths (38.5% of the test space) that remain uncovered by existing conformance frameworks.We formalize a four-axis verification protocol — admission control, deterministic recomputation, chain fork detection, and fork-matrix structural invariants — and demonstrate that independent implementations across three major agent frameworks (AutoGen, CrewAI, LangGraph) have converged to structurally equivalent mechanisms consistent with CCS invariants. Our results establish that runtime conformance is not an optional enhancement but a structural prerequisite for production-grade agent deployment.Keywords: MCP, LLM Agent, Conformance Validation, Protocol Verification, Formal Verification, Agent Runtime GovernanceRepository: github.com/CorrectoverWebsite: correctover.github.io