3 posts tagged with "AI Agents"

Watching an AI agent struggle with a LaTeX ampersand for the tenth time isn't just boring. It’s expensive. You’re sitting there watching your automation burn through your daily quota in real-time just because the LLM can’t remember a backslash.

I tried the usual prompt engineering voodoo. I even threw the "Pro" model at it, hoping the extra reasoning would bail me out. It did not 🥲

That’s when it clicked. Between the start of the session and the final compilation, there are so many intermediate steps that the agent inevitably hits the "lost in the middle" problem. By the time it’s actually time to fix the compile issues, it has forgotten the rules I painstakingly wrote into the system prompt!

I needed a way to inject the rules after the error happens but before the agent tries to fix it. I was about to do it manually—and honestly, at that point, I might as well have just written the LaTeX myself—but then I remember the new Skills feature in gemini-cli. It was exactly the approach I was looking for.

And now I have a blueprint for building AI agents that can reliably troubleshoot and fix their own mistakes with surgical precision!

I've been watching Gemini CLI development for a while, and I started noticing a pattern that felt... redundant. First, we got custom Slash Commands. Then Custom Sub-Agents. Now, we have Skills.

It started to feel like feature bloat. Why do we need three different ways to shove a prompt into the context window? Is this just marketing, or is there actual engineering logic here?

On the surface, it looks like a massive violation of SRP. If all three features are just dumping strings into the context window, why do we need three separate abstractions? Is this just feature bloat, or is there an actual architectural reason for the redundancy?

So off I went poking around the source code again. It turns out the real difference isn't in what they can do, but in how much work they make you do to keep the session (and your sanity) from collapsing.

Documentation tells you what the developers intended to share; the main branch tells you what they are actually building.

After accidentally discovering the undocumented sub-agent feature, I found myself watching Gemini CLI's repository with a closer lens. While reviewing the git logs on SHA d3c206c, I noticed a few mentions of "skills."

At first, I thought it might just be a marketing rebrand of custom agents. But the more I looked, the more it felt like a totally different approach to architecting Agentic AI workflows. I’ve been thinking of it as Delegation vs. Augmentation.

By understanding this distinction, you can stop stuffing your system prompts with every tool imaginable and avoid the unnecessary complexity of managing a swarm of sub-agents. If you can just make your current agent a bit smarter on the fly, why bother with all the extra overhead?