Posts and interview questions tagged MCP.
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.
Part 5: multi-tenant context engineering — tenant-scoped retrieval, per-tenant prompts and caches, noisy-neighbor fairness, model routing, and per-tenant cost attribution in C# + Python.
Part 4 : the enterprise design that makes GenAI production-grade — eval gates, injection + PII defense, cost control, and tracing.
Part 3: multi-agent architecture that survives production — a supervisor + specialized agents (C# orchestration, Python agent graph) with bounded loops.
Part 2: giving enterprise AI a memory that works — working, short-term, and long-term tiers in C# + Python, tenant-isolated, summarized, and governed.
Part 1 of a context-engineering series: why naive RAG hallucinates and the C#+Python context layer that fixes it — rewriting, re-ranking, budgeting.
How Mattrx swapped synchronous REST calls for Kafka — decoupling the ingestion pipeline, killing cascading failures, and cutting failures by 90%.