When AI agents access databases, APIs, and sensitive business operations, establishing cryptographic identity through X.509 public key infrastructure (PKI) becomes a critical requirement. The core challenge is proving which agent made a request, preventing forgery between agents, and confirming that each agent acts only on behalf of an authorized user. Traditional approaches such as sharing API keys between components or passing OAuth tokens through agent chains fall short of the zero-trust requirements that enterprise deployments demand.

Multi-agent AI frameworks increasingly use the Model Context Protocol (MCP) to connect agents to external tools and data sources. MCP defines how a large language model (LLM)-powered agent invokes tools such as database queries or email operations. However, MCP has no built-in authentication standard. It is transport-agnostic by design. This creates a critical gap: Without proper identity mechanisms, any component could impersonate an agent, replay intercepted credentials, or invoke tools without authorization.

In this post, we show you how to address this gap using Keyfactor EJBCA combined with AWS services. EJBCA is a PKI platform that supports high-availability deployment, REST API automation, and Online Certificate Status Protocol (OCSP) validation. The reference architecture uses four complementary security mechanisms. First, signed request envelopes provide cryptographic proof-of-possession on every agent call. Second, user-signed delegation assertions authorize agents without sharing credentials. Third, RFC 8693 audience-scoped tokens limit the scope of exposure from any single token disclosure. Fourth, real-time OCSP revocation checking propagates credential changes immediately across all agents.

The solution uses Amazon Bedrock for LLM inference through the AWS Strands Agents SDK .

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