5 posts tagged with "agents"

In the post about prompt, context and harness engineering I split the three layers apart and talked about the harness from the point of view of someone building an agent: idempotency, circuit breakers, approval gates, audit trails.

But most of us aren't building agents. We're using one: Claude Code, OpenCode, Cursor, Codex. Which raises the obvious question: if the tool already ships with a harness, what's left for me to do?

A lot. And it's exactly the part that changes results the most day to day.

If you use coding agents often, you've probably lived this scene: three terminals open, a Claude Code implementing a feature, a Codex reviewing another branch, an OpenCode investigating a bug. You go grab a coffee. When you come back, two of them have been sitting there for 15 minutes waiting for you to approve something, and one finished a while ago without you noticing.

Or worse: you accidentally close the terminal, the SSH connection drops, and the whole session goes with it.

Herdr solves exactly that. It describes itself as "where your coding agents live": a terminal runtime that keeps agents running in the background, shows the state of each one, and exposes an API so scripts (and other agents) can control everything.

Have you ever spent half an hour refining a prompt — making the instructions clearer, more detailed, adding examples, adding constraints — and the agent kept failing in exactly the same way?

You've been there: the model hallucinates even with a perfect prompt. Or worse: the agent acts on the world, does something it shouldn't, and you find out in production. Not in the test chat, not in the sandbox. In production, with real data.

That's not a prompt problem. And polishing the system prompt text won't fix it.

This article is about understanding where the problem actually lives — and why conflating these three layers of AI engineering wastes time, money, and sometimes causes incidents.

Technical documentation is a constant challenge in software projects. The technical team needs to document, but there's generally no dedicated tech writer. In this case study, I show how we created a complete automated documentation system using Cursor Agents, Rules, and Commands.

Software development is undergoing a significant transformation with the arrival of generative AI tools. Among these tools, Cursor stands out as one of the most powerful, especially with its Agents functionality.