This topic examines protocols and integrations used to manage credentials and secrets for autonomous AI agents—comparing purpose-built agent vault protocols (e.g., Inflectiv AVP) with Model Context Protocol (MCP) adapter patterns and other enterprise alternatives. As AI agents move from experiments to production in 2026, they increasingly need programmatic, auditable access to user credentials, API keys, encryption keys, and ephemeral tokens without expanding the organization’s attack surface. Key categories and representative tools include IAM/adapters (Keycloak MCP Server, AWS Cognito MCP, Okta MCP, Descope MCP) that expose user lifecycle and auth operations via MCP; enterprise key management and encryption integrations (Thales CipherTrust Manager, CRDP, CAKM) for EKM/TDE and cryptographic operations; attestation-first deployments (Attestable MCP) using TEEs and RA‑TLS to establish runtime trust; and proactive secret-detection services (GitGuardian) to stop leaks before they hit repos. These components illustrate two common approaches: (1) agent-focused vault protocols that centralize agent secrets management and policy enforcement, and (2) MCP-based connectors that let agents act through existing IAM/KMS infrastructure. Current trends emphasize short‑lived, policy‑driven credentials, runtime attestation, consolidated audit trails, and automated secret scanning. Trade-offs when comparing AVP-style protocols to MCP adapters include trust boundaries (who holds keys), interoperability with enterprise IAM/KMS, auditability, latency and availability for real‑time agent operations, and developer ergonomics. Evaluation criteria for teams should therefore include the trust model, support for ephemeral credentials and attestation, integration with existing key managers and audit systems, and controls for detection and remediation of leaked secrets.