Alan Walsh

Overview Claude Cowork brings powerful automation and agent orchestration to a simple desktop interface. I built automations with it that read folders, extract data from invoices, control a browser to interact with web apps, and generate deliverables like Excel sheets, PowerPoint decks, and Word reports. The underlying engine borrows the same planning and sub-agent approach … Read more

I built a set of agentic workflows to see how far coding agents can take routine automation. I wanted to answer a practical question: when should you use a coding agent like Claude Code to generate Python workflows, and when should you stick with visual workflow tools such as n8n? I tested real examples, deployed … Read more

I created an automation that turns short lived AI calls into long running, reliable agents. These agents can research, synthesize, and produce reports that span hours and many data sources. The trick was adding a control layer, which I call an agent harness, around the AI so it can plan, persist progress, and resume work … Read more

I built an automation that lets AI agents answer questions about structured data without hallucinating. It uses natural language query, not just vector stores. The result is a database agent that learns useful SQL patterns over time and reuses them when similar questions come up again. Why vector stores alone often fail for tabular data … Read more

I created an automation that can import tens of thousands of documents into an n8n RAG (Retrieval-Augmented Generation) system with a single click. I recorded this process for The AI Automators to show how to build an orchestrator workflow, run parallel ingestion jobs, and automatically recover from failures without manual intervention. This article breaks down … Read more

Introduction I created an automation that takes nearly any file type — documents, slides, spreadsheets, images, audio — and turns it into consistent, searchable content for a Retrieval-Augmented Generation (RAG) agent. In the accompanying walkthrough I show three practical ways to do this: LlamaParse for ease and breadth, Docling for a self-hosted route, and Mistral … Read more

I created an AI agent that can query databases effectively using natural language. This agent can handle everything from simple single-table queries to complex database schemas with multiple relationships. In this article, I’ll walk you through the entire process, demonstrating how to build such an agent using a Postgres database hosted on Supabase. While I … Read more

RAG agents are incredibly powerful tools for retrieving information from vector databases, but they often face a common challenge: the data retrieved can be outdated or irrelevant because it lacks precise filtering based on metadata. To solve this, I created an automation that allows agents to generate advanced metadata filters dynamically. This ensures they pull … Read more

In building AI-powered Retrieval-Augmented Generation (RAG) agents using n8n, I discovered a simple adjustment that can dramatically improve their effectiveness. This tweak takes just about ten seconds to implement but can make a significant difference in how well your agents understand and retrieve information. Even experienced users might overlook this detail, yet it’s one of … Read more

Context engineering has become a crucial skill for anyone working with AI agents today. As these agents grow more advanced and capable of independent operation—accessing the web, knowledge bases, and various tools—the traditional approach of prompt engineering no longer suffices. The key challenge is managing the context window, which is the limited amount of text … Read more