Posts and interview questions tagged Clean Architecture.
A valid token proves who an agent is, not what it may do. Here's scoped, least-privilege authorization for MCP tools — identity plus policy.
An API key is not MCP authentication. Here's how to secure MCP servers as OAuth 2.1 resource servers with Entra ID — tokens, audiences, and identity.
Most MCP tools mirror your REST API and confuse the model. Here's how to design task-shaped tools an agent picks right, calls cleanly, and trusts.
The server exposes tools; the client drives them. Here's a production MCP client — discovery, the agent loop, multi-server routing, and reconnects.
A demo MCP server is a weekend; a production one needs typed tools, real error handling, pagination, and health checks. The Mattrx build, end to end.
MCP has a host, clients, servers, three primitives, and a capability handshake. Here is the full architecture, mapped onto Mattrx's real Azure setup.
Every AI agent you build needs every backend you own — that is N×M glue code. MCP turns it into N+M. Here is why, with real Mattrx production numbers.
Naive AI review drowns devs in false positives until they ignore it. Here's the context-aware, adversarially-verified pipeline we run on every Mattrx PR.
Delivering 15M webhooks a day to endpoints you don't control is deceptively hard. Here's the outbox + queue + retry design that never drops an event.
Chunk-and-embed RAG hits a wall at scale. Open Knowledge Format (OKF) feeds a context engine structured, governed knowledge — the Mattrx rebuild with code.
Most enterprises bolt AI onto a backend built for CRUD. We rebuilt Mattrx around nine AI-native layers in production. Here is the blueprint, with code.
Part 6: AI & data governance as a control plane — classification at ingestion, entitlement-aware retrieval, consent & purpose limits, data lineage, and policy-as-code in C# + Python.