Daniel Walsh

When an AI agent goes looking for information and comes back empty-handed, the usual assumption is that the answer was missing. A lot of the time, that is not the real problem. The answer may have been sitting right there in the source material. It just didn’t survive the trip into a machine-readable format. That … Read more

Every new model release brings the same promise. Better reasoning. Better synthesis. Better scores on the benchmarks. And yet, if you build knowledge agents for real work, the failures that matter most still keep showing up. I see this especially in retrieval-based systems. The model pulls in source material, then produces something that looks polished … Read more

Last month, Microsoft researchers published a paper with a blunt headline: large language models corrupt your documents when you delegate work to them. I think this matters a lot more than most AI coverage suggests. Right now, much of the conversation around AI still revolves around coding. That’s important, but it’s also narrow. The bigger … Read more

Over the last year, I’ve seen a steady stream of headlines declaring that RAG is dead. Then the proposed replacement changes every few weeks. Sometimes it’s long context windows. Sometimes it’s agentic file exploration. Sometimes it’s context engineering, memory systems, or agent skills. Most of those headlines are too dramatic. But something genuinely interesting did … Read more

Three of the biggest names in AI all made moves in the agent space within a single week. Anthropic launched Claude Managed Agents. LangChain answered with Deep Agents Deploy. Then OpenAI shipped the next evolution of its Agents SDK. At first glance, these looked like three separate announcements. They weren’t. They were three different answers … Read more

Anthropic recently published a deep dive on harness design for long-running AI agents. The topic sounds narrow, but the lessons apply to almost any specialized agent system I build, from contract review to compliance audits, risk analysis, content pipelines, and impact assessments. The core idea is simple but easy to miss: an AI model alone … Read more

In 2026, a lot of people expect AI to finally deliver real work and real business value. Not “AI wrote a blog post.” Not “AI drafted a few social messages.” I’m talking about AI systems that can run complex, multi-stage workflows that normally involve humans, spreadsheets, approvals, and a lot of domain knowledge. Examples include … Read more

I created an automation that lets powerful cloud language models answer questions about private company documents without ever seeing real sensitive data. The cloud model only ever receives surrogate values. Real names, passport numbers, financials and other secrets stay inside my network. This article explains how I put that together, the challenges I ran into, … Read more

Agent skills have become one of the most useful design patterns for building practical AI agents. I created an automation that combines agent skills with code execution sandboxes inside a custom Python and React agent. The result is an agent that can not only answer questions but follow step-by-step workflows, execute code safely, and produce … Read more

I created an automation that dramatically reduces unnecessary LLM context and lets the model orchestrate complex workflows with real code. The two design patterns at play are simple in concept but powerful in practice: tool search and programmatic tool calling. Both solve problems that crop up as agents grow: tool definitions bloating the context window, … Read more