Production AI is a different problem than prototype AI. I write about the gaps between them: evaluation systems that catch failures before users do, agent architectures that fail gracefully, retrieval pipelines that hold up under real query distributions, and the engineering decisions that most tutorials skip.
A high-level framework for integrating agentic AI with real-time analytics pipelines, covering architecture, governance, trade-offs, risks, and evaluation across finance, IoT, and healthcare.
agentic-ai
real-time-analytics
data-products
streaming
Not all problems an AI agent solves are the same. This piece unpacks the distinction between Skills (knowledge layer) and MCP (execution layer) — and why separating them leads to more efficient, maintainable agent systems.
mcp
agent-skills
context-engineering
ai-architecture
A production-grade guide to agent evaluation: single-turn unit evals, trajectory scoring, multi-turn simulation, harness engineering, and LangSmith-driven continuous improvement loops.
langchain
langgraph
langsmith
evaluation
