Insights on AI agents, automation, developer tooling, and human–AI collaboration. Guides, tutorials, and industry analysis from the cowork.ink team.
Articles - Page 6
Cursor Agent Mode: How to Let AI Build Features Autonomously - Cursor Agent Mode turns your AI coding assistant into an autonomous developer that plans, writes, runs tests, and ships pull requests — all while you work on something else. Here's everything you need to know to use it safely in 2026.
How to Build an MCP Server: Step-by-Step Tutorial (2026) - Model Context Protocol (MCP) has become the standard way AI agents connect to external tools, APIs, and data sources — with 6.9 million npm downloads per week. This step-by-step tutorial shows you how to build your own MCP server in TypeScript, register it with Claude and Cursor, and expose real tools your agents can use.
MCP vs. A2A: Comparing AI Agent Communication Protocols (2026) - Two protocols now define how AI agents connect to the world: MCP (Model Context Protocol) by Anthropic lets agents access tools and data, while A2A (Agent2Agent) by Google lets agents talk to other agents. Here is exactly how they differ — and how to use them together.
n8n vs. Zapier vs. Make for AI Agents: Which Platform in 2026? - n8n, Zapier, and Make all support AI agent workflows — but they approach it very differently. We compare pricing, AI capabilities, self-hosting, and real costs at scale to help you pick the right automation platform in 2026.
Persistent vs. Ephemeral AI Agents: Why Memory Makes Agents 4x Better - Ephemeral AI agents forget everything the moment a session ends. Persistent agents accumulate context across every interaction — and the performance gap is staggering. Here's what each type is, when to use each, and how to choose.
Prompt Caching for AI Agents: Save 70%+ on API Costs - Prompt caching stores the processed version of your AI agent's system prompt so the LLM skips recomputation on every request. This guide covers how prompt caching works across OpenAI, Anthropic, and Google — with real pricing data, code examples, and battle-tested patterns that cut API costs by 70–90%.
Reactive vs. Proactive AI Agents: Understanding Agent Behavior Types - Reactive AI agents wait for you to ask. Proactive agents anticipate what you need and act before you realize you need them. This guide explains the difference, when to use each, and why the best systems combine both.
SWE-bench Explained: How We Benchmark AI Coding Agents in 2026 - SWE-bench is the standard benchmark for measuring how well AI agents solve real-world software engineering tasks. This guide explains how it works, what the scores actually mean, and why no single number tells the full story.
Types of AI Agents: A Complete Classification Guide - Not all AI agents are alike. From simple reflex systems to autonomous multi-agent networks, this guide maps every major classification — with real examples and a framework for choosing the right type for your use case.
Context Engineering for AI Agents: Beyond Prompt Engineering - Most AI agent failures are not model failures — they are context failures. Context engineering is the discipline replacing prompt engineering in 2026: designing the entire information environment your agent operates in.
n8n AI Agents: Build Agentic Workflows Without Writing Code - n8n's AI Agent nodes turn a visual automation tool into a full agent builder — with tool calling, memory, sub-agents, and MCP support. This guide walks you through building your first agentic workflow, from setup to production patterns.