Secure credential management for AI agents focuses on giving large language models and assistant frameworks controlled, auditable access to secrets, keys and authentication flows without exposing raw credentials to the model. This topic is timely in 2026 because AI agents are increasingly integrated into enterprise workflows, expanding the attack surface for secrets while regulatory and compliance pressures (auditability, least privilege, encryption-at-rest) have intensified. Model Context Protocol (MCP) servers provide a practical pattern: they act as a constrained intermediary that exposes narrowly scoped tools to an agent. Enterprise-grade examples include Thales CipherTrust MCP servers (for key management and cryptographic operations), Thales CDSP CAKM and CRDP MCP servers (for Database EKM/TDE and RESTful data protection), and identity-focused MCPs that surface Keycloak, Okta, AWS Cognito or Descope functionality for authentication, user lifecycle and audit search. An Attestable MCP variant runs inside a trusted execution environment and supports remote attestation (RA‑TLS/TEE), helping clients verify server integrity before releasing high‑value secrets. Key trade-offs center on control versus latency: centralized key managers and EKM/TDE integrations (CipherTrust) provide strong key governance and separation of duty, while identity providers (Keycloak, Okta, Cognito, Descope) simplify user and session management for agent-driven flows. Attestation and TEEs mitigate trust assumptions but add operational complexity. For practitioners, the current best practices are: introduce MCP proxies to limit credential exposure, integrate with enterprise KMS/EKM for key lifecycle and auditing, and consider attested execution when secrets release policies require verifiable runtime integrity. These patterns help organizations adopt AI agents while maintaining credential security, compliance and traceability.