AI Development and Engineering covers how to build with Claude Code, coding agents, LangChain, and modern agentic frameworks. The guides here focus on patterns that hold up in production: prompt design, tool use, evaluation, retrieval, and the day to day workflow of shipping AI features rather than toy demos.
Claude Dreaming consolidates agent memory between sessions. How it works, the architecture changes it asks for, and where it actually moves the numbers.
How to package commands, agents, skills, hooks, and MCP servers into a single Claude Code plugin your team or customers install once. Real structure, marketplace.json, and what belongs inside.
Deep Agents v0.5 async subagents return a task ID instead of blocking the supervisor. The Agent Protocol wiring, mid-task steering, and where the pattern breaks in production.
A production pattern for running parallel Claude Code sessions with --worktree. Syntax, subagent isolation, the shared-state traps worktrees don't solve, and how to clean up.
Running Claude Code in CI/CD without the surprise bills. Headless flags, JSON output parsing, cost controls, and the failure modes nobody writes about.
Claude Code subagents run parallel AI work without context chaos. Config, dispatch patterns, model choice, and the mistakes that waste tokens, with examples.
Configure Claude Code beyond defaults: skills, hooks, subagents, and CLAUDE.md memory. Working examples, team patterns, and common setup mistakes to avoid.
How to use LangGraph for stateful AI agents that branch, loop, retry, and pause for human approval, with code for state, cycles, and checkpointing.
Build autonomous AI agents for CI/CD with LangGraph: code review, test generation, security analysis, and deployment decisions, with the operational patterns that keep them honest.