How to create an ai agent that actually does something useful, not just a demo?
I've been reading about AI agents for a while now and every time I go down the rabbit hole I end up with the same feeling: these things look impressive in a controlled demo and then fall apart the moment you try to apply them to a real workflow.
Most of the tutorials I've found on how to create an AI agent are either toy examples (summarize this PDF, answer questions about a CSV) or they're so abstract that I can't figure out how to map them to an actual business process. My team handles a pretty complex sales ops workflow with data spread across a CRM, a few internal tools, and some manual handoff steps that nobody has ever properly documented. The idea of an agent that could handle even part of that is appealing, but I'm genuinely skeptical that the tech is there yet outside of well-funded enterprise pilots.
Has anyone actually deployed something that runs in production, not just a p...
AI agents are horrible with dashboards and grids. This got it there in one prompt
Hey guys,
More devs are using agents for UI work, and they seem solid enough for a lot of it.
But things often get messy when dealing with complex grids and data-heavy dashboards.
You ask for grouping, filtering, or accessibility, and the agent often gives you something that kind of works.
But the code is messy, so you either end up repeatedly prompting to get it right or clean the code yourself.
That is what we have spent a lot of time solving with LyteNyte Skills, which can drastically reduce the time and tokens you spend building out grids/dashboards.
The options dashboard shown was built with Claude Code and LyteNyte Grid Skills.
It’s fully functional.
You can expand it, group it, sort it, and filter it.
It is also fully accessible.
I used one prompt:
...
AI has made my job much easier and that worries me, a lot
I'm a senior level FE-Engineer at a large e-commerce company with a complex mono-repo codebase. Ever since our company handed out Claude accounts most of my co-workers and I have been using Opus pretty much every day. It often feels like I don't code anymore so much as manage a very talented junior engineer.
For patterns in the code that have already been established, I'd say it has a about a 95% success rate on a given task, with occasional follow-up prompts to cover anything it's missed.
For new features it maybe gets 70% of the way there and I have to go in and fix or add the rest. It's still terrible at CSS.
All of this still requires a senior level engineer like myself to write intelligent prompts, answer AI questions, and review. So, to that extent I don't believe a PM is going to vibe code my job away.
What I'm concerned with is how much faster I'm now getting my work done. AI...
How AI Is Reshaping Automotive Electronic Software Architecture Design? A Comprehensive Analysis of the Software Architecture Design Agent
For teams working on automotive electronic software architecture, the biggest fear is not the architecture design itself, but that once the design is done, the documentation has just begun.
A hundred-page requirements document, a set of customer-specific templates (Word + Excel), plus ASPICE SWE.3, functional safety ASIL allocation, layered architecture (application layer → service layer → abstraction layer → driver layer) and other specification requirements—architects need to break down requirements one by one, draw layered diagrams, define interface matrices, write dynamic sequences, and mark requirement traceability.
One round of writing for one version of requirements, another round when requirements change, always playing catch-up.
Software architecture documentation is becoming an increasingly prominent bottleneck in the automotive electronics R&D process.
It is slow becau...