Practical writing on AI agents, SDLC documentation automation, full-stack architecture, and product validation.
29 results
A practical guide to goals, permissions, verification, and escalation rules for teams adopting coding agents such as Claude Code and Codex.
As AI agents become better at using tools, the bottleneck shifts from model quality to permission design. This post lays out a least-privilege operating model for MCP-based tool use.
A practical explanation of vector databases, how they differ from traditional databases, and why they matter for AI search, RAG, and recommendation systems.
A technical explanation of how vector databases store embeddings, search them with ANN, and match results back to original documents.
If MCP standardized tool connectivity, permissions, policy, audit, and isolation are what make production AI agents safe.
MCP, multi-agent workflows, and tool integrations are hot right now, but production systems live or die by context, state, and control planes — not the protocol alone.