Protocols and infrastructure that cut across every agent framework — where Prefactor's spans and controls attach regardless of which agent framework sits on top.
Model Context Protocol — the open protocol connecting agents to tools, data, and services through a standard interface. The most active area of the Prefactor blog: authentication, authorization, and audit patterns for MCP servers and clients in production.
The open-source, vendor-neutral standard for collecting traces, metrics, and logs from distributed systems — applied to agents, it captures tool calls, latency, and token usage in a form that stays portable across observability backends.
The embeddings-and-similarity-search layer behind RAG-based agents — governance here means access control on what an agent can retrieve, freshness of the underlying data, and auditability of every query.
An emerging open protocol for agents to describe capabilities and exchange tasks across frameworks and vendors — relevant wherever one agent needs to call another it doesn't share a codebase with.
A centralized proxy between agents and foundation model APIs — the natural place to enforce consistent authentication, rate limiting, cost tracking, and audit logging regardless of which model or provider an agent calls.
Prefactor helps teams observe, evaluate, and improve their AI agents in production — across every framework and provider.