Hard-won lessons in
enterprise AI
Architecture patterns, honest failure post-mortems, and opinionated takes on building AI systems that survive contact with production.
Why Multi-Agent Systems Fail in Production — and How to Fix Them
Most enterprise AI agent systems work beautifully in demos and break in production. Here are the architectural patterns that actually hold up at scale.
The RAG System Maturity Model: From Prototype to Enterprise Grade
Building a RAG demo is easy. Building one that handles 10K queries/day with measurable accuracy is the real engineering problem.
LLM Cost Optimization at Enterprise Scale: A Practical Guide
When you're processing millions of tokens per day, inference cost becomes an engineering constraint. These patterns cut LLM costs 60–80%.
Model Context Protocol in the Enterprise: What You Need to Know
MCP is how AI agents talk to tools. Here's what enterprise teams should understand about security, scoping, and production deployment.
Human-in-the-Loop AI: Designing Systems That Know Their Limits
The most dangerous AI systems don't know when to ask for help. Here's how we design escalation paths that earn organizational trust.
Event-Driven Architecture for AI Systems: The Missing Piece
Most AI architectures are synchronous when they should be event-driven. This mismatch creates brittleness, scaling failures, and debugging nightmares.
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