coding-agents 30
- The BMAD Method — Structured Multi-Agent Development with AI
- Technical Debt in AI-Generated Codebases — What It Looks Like and How to Manage It
- Testing Non-Deterministic Systems — Strategies That Actually Work
- AWS Kiro — The IDE Built Around Specs, Not Prompts
- Spec-Driven Development — Why Specs Are the New Source of Truth
- AI Code Review at Scale — Moving Beyond the Single-File Review
- Spec-Driven Development — Writing Specs That AI Agents Can Execute
- Self-Healing CI — Agentic Pipelines That Fix Their Own Failures
- Autonomous PR Agents — How to Review Code When AI Opens the PR
- Beyond Vibe Coding — What AI-Native Engineering Actually Looks Like
- CrewAI for Enterprise Multi-Agent Workflows
- Building Production Agents with LangGraph — A Hands-On Walkthrough
- Tool Orchestration at Scale — Beyond Simple Function Calling
- Long-Term Memory Patterns for Production Agents
- Multi-Model Orchestration — SLM + LLM Hybrid Architectures
- Reasoning Models for Agents — When Thinking Tokens Are Worth It
- Computer Use Agents — The New Agentic Paradigm
- Stateful Agents — Managing State in Production
- Evaluating RAG Pipelines — The Metrics That Matter
- Chunking Strategies That Actually Work in Production RAG
- Vector Databases in 2026 — Which to Use and When
- Context Window Management in Production Agents
- RAG vs Fine-Tuning — The Hybrid Answer in 2026
- RAG in Production — Beyond the Basics
- Evaluating Coding Agent Quality — Beyond 'Did It Run?'
- Multi-Step Coding Agents — Patterns and Failure Modes
- Agent Memory and Context Management — The Hard Part
- Tool Use in AI Agents — Patterns That Work in Production
- Building Your First Coding Agent — A Practical Walkthrough
- What Makes a Good Coding Agent — Design Principles