Practical writing on applied AI systems, agent reliability, retrieval, integration architecture, evaluation, and production engineering.
A practical eval workflow for finding failures from context packaging, rendering changes, and prompt-path drift before LLM features ship.
Founder
A practical security model for MCP servers and agent tools: task-scoped capabilities, mediation, deception, and failure handling that keep agents inside hard trust boundaries.
Founder
A production guide to making ANN plus metadata filters fast in enterprise RAG, with patterns that avoid full scans and unstable recall.
Founder
A practical evaluation stack for agent systems: collect traces, use feedback to find pain points, add human labels, and apply LLM judges with calibration.
Principal Consultant
How to sandbox, gate, and instrument AI coding agents before giving them repo write access or a shell.
Principal Consultant
Production MCP needs more than protocol support: contract tests, capability policy, trust scoring, and hardened auth and session handling.
Principal Consultant
The hardest production problem with AI agents is not adding more tools. It is making the system reliable, secure, observable, and cheap enough to trust.
Principal Consultant
Model Context Protocol has become a useful interface layer for tools, but the production work starts after the demo: auth, context shaping, multi-server routing, and streaming semantics.
Principal Consultant
A retrieval step can improve an AI application, but it can also increase cost, latency, and hallucination surface area. The real engineering question is when retrieval earns its place.
Principal Consultant
