MCP server design for internal AI tools
How we scope Model Context Protocol tools around narrow schemas, permissions, audit logs, local testing, and human approval.
Technical notes from the edge of practical AI usage and systems.
Field notes for teams turning AI from scattered experiments into workflows, agents, tools, review loops, and operating systems that people use.
MCP agents and function calling
How tool-calling agents differ from MCP-connected workflows, and where each pattern fits in internal AI systems.
AI workflow audit for small businesses
How to map inputs, decisions, tools, memory, outputs, review steps, and failure modes before choosing what to automate.
Context engineering for large codebases
How coding agents get better when repo instructions, retrieval, dependency maps, and definitions of done are treated as infrastructure.
The three phases of AI deployment
A practical model for moving from workflow audit to pilot build to controlled rollout without forcing every engagement into the same timeline.
RAG evaluation for internal knowledge bases
How to separate retrieval quality, answer quality, source freshness, faithfulness, and human trust before deploying company knowledge AI.
LLM retrieval for grounded answers
How retrieval, citations, context limits, and answer checks work together when teams need AI outputs grounded in source material.
AI governance lite for SMBs
How smaller teams can set data rules, approval tiers, vendor review, incident paths, logs, and human review without building a bureaucracy.
AI code review with judge agents
How reviewer agents, severity rules, false-positive tracking, and PR handoff make automated code review useful inside engineering teams.