Skip to content
Topics

AI Agents & Automation

Understand AI agents, RAG, and automation workflows. From concepts to real-world applications and implementation guides.

34 articles

Sort articles to find what you need

What Is a Multi-Agent System? Patterns, Frameworks, and When to Actually Use One

What Is a Multi-Agent System? Patterns, Frameworks, and When to Actually Use One

In 2026, the AI agent conversation has shifted from "one super-agent" to "a team of agents with different roles." Anthropic Research, Claude Code subagents, Devin, and Cursor's parallel workers are all multi-agent. This article covers the definition, the five core architecture patterns (orchestrator, handoff, hierarchical, peer-to-peer, pipeline), a comparison of the big-four frameworks (Claude Agent SDK / OpenAI Agents SDK / LangGraph / Strands), production examples, the cost structure (Anthropic reports ~15x tokens), when to use it and when not to, and design best practices — all grounded in official sources.

GPT-5.5 vs Claude Opus 4.7: A Practical Head-to-Head — Benchmarks, Coding, Agents, Pricing, How to Choose

GPT-5.5 vs Claude Opus 4.7: A Practical Head-to-Head — Benchmarks, Coding, Agents, Pricing, How to Choose

In April 2026, Anthropic Claude Opus 4.7 and OpenAI GPT-5.5 shipped one week apart. Opus leads on real codebase work (SWE-bench Pro 64.3%); GPT-5.5 leads on terminal control and customer support (Terminal-Bench 82.7%, OSWorld 78.7%) — almost mirror-image strengths. And while Opus has the lower sticker price, output token volume often makes GPT-5.5 about a quarter the real-world cost on the same task. This article lays out the spec sheet, benchmark deep dive, token-economics, strengths-and-weaknesses map, use-case picks, and a dual-vendor strategy, all grounded in official sources and third-party evaluations.

What is Harness Engineering? Designing the Layer Around the LLM in the AI Agent Era

What is Harness Engineering? Designing the Layer Around the LLM in the AI Agent Era

The center of gravity has shifted from prompt engineering to harness engineering — the new battleground of the AI agent era. This article lays out what harness engineering actually is, how it differs from prompt engineering, the six components (tool definition, context management, memory, loop, guardrails, output UX), a side-by-side comparison of Claude Code, Cursor, Codex CLI, and Devin, and a practical design checklist — the foundation you need to use or build AI agents seriously.

Why AI Agents Ignore Your .md Rules — And How to Make CLAUDE.md, Cursor Rules & AGENTS.md Actually Stick

Why AI Agents Ignore Your .md Rules — And How to Make CLAUDE.md, Cursor Rules & AGENTS.md Actually Stick

AI agents (Claude Code, Cursor, Copilot, Codex) ignoring your .md rule files comes down to 5 root causes: context-window limits, auto-compact diluting early instructions, fuzzy priority, vague phrasing, and bloated scattered files. This article walks through diagnostics, quick wins (compress to under 150 lines, priority markers), and longer-term systemization with Claude Code Hooks, sub-agents, and custom slash commands — plus tool-specific best practices.

ChatGPT 5.5 (GPT-5.5) Release: Features, Benchmarks, Pricing & Claude Opus 4.7 Comparison

ChatGPT 5.5 (GPT-5.5) Release: Features, Benchmarks, Pricing & Claude Opus 4.7 Comparison

OpenAI shipped "ChatGPT 5.5 (GPT-5.5)" on April 23, 2026. Pitched as "a new class of intelligence for real work and AI agents," it scored 82.7% on Terminal-Bench 2.0 — pulling ahead of Claude Opus 4.7 (69.4%) and Gemini 3.1 Pro (68.5%) to reclaim the top spot. But API pricing doubled vs GPT-5.4 ($5/$30 per MTok), and Claude Opus 4.7 still beats it on SWE-Bench Pro. This article gives you the full picture — features, benchmarks, pricing, plan availability, head-to-head with Claude and Gemini, and how to pick — all grounded in official sources.

What Is RAG? A Beginner-Friendly Guide to How It Works and What It Does

What Is RAG? A Beginner-Friendly Guide to How It Works and What It Does

You want ChatGPT to read your internal docs and answer questions about them --- that is exactly what RAG (Retrieval-Augmented Generation) is built for. This article walks through how RAG works in three steps, covers vector databases, a LangChain implementation, and when to pick RAG over fine-tuning. We also showcase real use cases including internal Q&A, customer support, and legal/medical knowledge work.

Will Claude Code and Codex Make Infrastructure & Network Engineers Obsolete? The Reality AI Is Reshaping

Will Claude Code and Codex Make Infrastructure & Network Engineers Obsolete? The Reality AI Is Reshaping

Now that Claude Code and OpenAI Codex can auto-generate infrastructure code (Terraform, Docker, Ansible, and more), some people are asking: "Are infrastructure engineers about to become obsolete?" The reality is more nuanced. This article maps out what AI is actually good at, the areas where only humans can take ownership — physical work, incident judgment, security accountability — and how infra engineers should evolve in the AI era.