Difficulty in managing AI agent workflows and ensuring task definitions are clear

Signals over time

3 observed signals across 1 sources, tracked for 7 days. Confidence: low.

What people are saying Observed

• “I love Rich Hickey's speeches, and found this entertaining, but it seems pretty pedantic. I mean, I guess there's a lot of people that don't know the difference in what he's saying. `rm` is a tool. You don't tell `rm` to delete things and then get annoyed when it deletes your production database because it inferred incorrectly. It does what you tell it. Like a hammer hits what you hit with it. `claude` can be considered a version of `rm` that can do what `rm` does (and other things - including things you don't want). Nearly everyone using it is aware of that. An employee is not a `tool`. `claude` is closer to an employee. You tell it to do things. It costs you money. Sometimes you get what you wanted, sometimes you get something far better, sometimes you get literally nothing, other times you get disaster (more often with `claude` than with a good employee). That's why they call it an `agent`, not a `tool`. Employers don't throw up their hands and say, no thanks, I'll do everything myself with `tool`s. They say, I'll try to learn how to better manage employees which are useful to further my goals (along with tools - because both are”

  Hacker News · frustration

• “Building Mia AI: Making AI-Agent Execution Visible, Reliable, and Safe. Suggested tags: agents, API, tools, orchestration (select the closest available tags). Hello OpenAI Developer Community, I’m building Mia AI , a visual AI workspace designed to make working with AI agents more intuitive, transparent, and useful. The idea is to give users more than a chatbox. Mia should help them describe a goal, break it into actionable steps, coordinate digital tools, show what is happening during execution, and explain the final results. I’m especially interested in solving a few engineering challenges: Reliable execution: How should an agent plan and execute multi-step digital tasks without losing state? Tool integration: What patterns work best for connecting APIs, GitHub, and other external tools? Transparency: How can the interface show genuine task progress, tool calls, failures, and outcomes rather than simulated activity? Safety and control: How should permissions, human approvals, retries, and audit logs be designed for actions with real-world consequences? My goal is to build an agent experience that is genuinely capable, fast, precise, and extensible, while keeping users in control ”

  RSS Feeds · request

• “This repo I started yesterday has most of the patterns I like to use: https://github.com/mbcrawfo/SqlSource Really the single biggest piece I use today is using a tool like superpowers to ensure your task is well defined. Added bonuses are its TDD implementation skills and multiple review steps baked into the workflow. Before I discovered superpowers I was manually telling claude to do these workflows, the plugin makes it easy. In the repo itself: * Keep agents.md focused; claude likes to really bloat it by default. The "Adding guidance for AI agents" rules go into every project and have worked pretty well. * Aggressively use nested agents.md files. Focused rules located where they're relevant seem to do much better than having a large whole-project rule dump in the root agents.md. * Recently I've started using the tech debt and deferred work documentation rules. Claude frequently decides on its own that some piece of functionality or issue is "deferred until later" or "out of scope". These rules help make it clear so that you can make it fix the item or at least have it documented for a followup session. * Have it test al”

  Hacker News · frustration