Insights on full-stack development, serverless architecture, and business impact.
26 results
When coding agents become part of team operations, prompts are not enough. This guide shows how to connect traces, decision logs, evals, and regression checks into an improvement loop for AI-native development.
To use coding agents more often, teams do not need heavier supervision. They need clearer boundaries. This guide turns permissions, network access, worktrees, and CI gates into a practical sandbox operating model.
Claude Code, Codex, and other coding agents do not need longer prompts as much as they need better delegation packets. This practical guide shows how to define outcome, scope, permissions, verification, and stop conditions before handing work to an agent.
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.