AI Workflows

AI agents, LLM pipelines, and intelligent automation for production systems.

AI workflows sit at the intersection of language models, agent frameworks, and automation infrastructure. This pillar covers how to build, deploy, and operate AI-powered systems: from single-prompt pipelines to multi-agent orchestrations that plan, execute, and verify autonomously. Guides here explore MCP server integration, Claude Code and Cursor workflows, RAG pipelines, and the governance layer that separates reliable agents from fragile demos. Each article is grounded in practitioner experience and benchmarked data, with clear architectural recommendations for teams moving from prototype to production.

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Layered diagram showing the AEO and GEO editorial layer sitting on top of the Agent Legibility substrate, with eleven dimmed guide tiles and one illuminated tile representing the eight-point-three percent of guides that mention the prerequisite

The substrate before the editorial layer

AEO and GEO Explained: The Agent Legibility Prerequisite

AEO and GEO are the editorial layer of AI search visibility. Agent Legibility is the substrate underneath. Most AEO guides skip the prerequisite. This one starts there.

Automation Switch

We Audited 103 Publisher Sites for Agent Legibility: Here Is What We Found

Best Web Scraping APIs for AI Agents and MCP Builders

Dark hero showing a SKILL.md file on a cream card with an amber magnifying-glass overlay highlighting suspicious frontmatter fields, next to a pass/fail verification checklist.

36.82% of community agent skills contain security flaws. Here is how to find them before they find you.

Agent Skills Security Audit Guide: How to Verify SKILL.md Files Before You Install

36.82% of community agent skills contain security flaws. 76 confirmed malicious payloads. A 15-point audit checklist, 8 vulnerability types with CVEs, and frameworks for verifying SKILL.md files.

Apr 2026·28 min

Dark hero showing a grid of starter SKILL.md template cards labelled by stack with a central install command card and amber accent lines connecting stacks to templates.

Copy-paste starter templates for every major stack.

SKILL.md Templates by Project Type: Starter Configs for Every Stack

Copy-paste SKILL.md templates for Next.js, Python, Go, React, DevOps, and data projects. Each template follows the four-section anatomy and stays within token budget limits.

Apr 2026·18 min

Dark hero image of a terminal window installing SKILL.md files, with a stack of labelled skill cards arranged as a grid around the command output.

The skills your agent should already have.

21 Best SKILL.md Files Every Developer Should Install

21 production-tested SKILL.md files across code review, testing, content, DevOps, security, and API design. Source, friction eliminated, and install command for each.

Apr 2026·22 min

Dark hero showing MCP protocol architecture split into a solid auth layer and an open surface covering rate limiting, input validation, and audit logging.

The protocol is everywhere. The security model is half-finished.

MCP Security: What the Spec Covers and What It Leaves to You

MCP connects AI agents to external tools. The auth spec is solid. The attack surface around it is wide open. Here is what the protocol enforces, what it leaves to you, and how to close the gaps.

Apr 2026·16 min

Dark hero showing three manifest files side by side on cream cards — SKILL.md, AGENTS.md, CLAUDE.md — with amber VS badges between them and the question When to use each? as headline.

Three files. Three purposes. One system that actually works.

SKILL.md vs AGENTS.md vs CLAUDE.md: When to Use Each

CLAUDE.md sets project-wide defaults. AGENTS.md governs multi-agent behaviour. SKILL.md teaches a specific task. A decision matrix and project structure guide for choosing the right file.

Apr 2026·18 min

PydanticAI agent code alongside LangGraph StateGraph code, split-screen comparison of two Python agent frameworks

Two production philosophies

PydanticAI vs LangGraph: Two Different Production Bets

PydanticAI brings type-safe, injection-first agents. LangGraph brings stateful graph execution. These are distinct production bets, each shaped by a different philosophy.

Apr 2026·12 min

Dark canvas header showing four agent framework names arranged as a branching decision path with LangChain, CrewAI, AutoGen and LangGraph as endpoints

LangChain vs CrewAI vs AutoGen vs LangGraph: Which AI Agent Framework Should You Actually Use?

Four frameworks, four different production problems. LangChain for breadth, CrewAI for prototyping speed, AutoGen/MAF for enterprise, LangGraph for execution control. Here is how to choose wisely.

Apr 2026·12 min

13 editorial components arranged in a systematic grid on a dark background with amber and blue accent colours, representing the AutomationSwitch component library architecture

13 components. 5 profiles. One system.

How We Built a 13-Component Library That Structures Every Article on This Site

A codified component library with placement rules, justification tests, and content profiles governs every article on AutomationSwitch. Here is the full system and how to build your own.

Apr 2026·12 min

Step-by-step visual guide showing the progression from a simple Python script to a working AI agent with tool-calling and MCP integration

Building Your First AI Agent: A Step-by-Step Guide

Go from zero to a working AI agent in under 100 lines. Tool-calling, reasoning loops, and MCP integration with practical Python code you can copy, run, and extend.

Apr 2026·15 min