Smallstep AI Security for Autonomous Systems is a cryptographic identity platform designed to secure AI agents and MCP (Model Context Protocol) toolchains. It replaces API keys and implicit trust mechanisms with short-lived, hardware-backed certificates that establish verifiable identity for non-human actors such as AI agents, MCP clients, MCP servers, and model runtimes. The platform addresses security challenges specific to autonomous AI systems, including credential sprawl from long-lived API keys, unverifiable access to internal tools and data, tool aggregation risk via MCP servers, and shadow AI access from unmanaged devices. Core to the platform is ACME device attestation, which anchors cryptographic trust directly in hardware using secure elements such as TPMs (Trusted Platform Modules) or Secure Enclaves. Devices must prove their identity through hardware attestation before certificates are issued, eliminating reliance on reusable secrets. The Identity Control Plane centralizes the identification, authentication, and authorization of devices, workloads, and services. It replaces fragmented credential handling with a unified system of record for cryptographic identity, policy, and lifecycle enforcement. The platform also provides selective Zero Trust Network Access (ZTNA), enabling continuous and contextual access decisions based on device identity, posture, and policy—without interrupting users or automated workflows. Certificates issued are short-lived and automatically rotated, are non-exportable, and carry cryptographic provenance, contrasting with API keys which are long-lived, copyable, and lack execution context. The platform integrates with existing identity providers, infrastructure, and security tooling, extending trust controls to AI runtimes and MCP-based systems.