u/Extension-Tip8921

This is one of the things that made me rethink how I give agents outside information.

If I’m asking about a restaurant, generic web results are fine. If I’m asking about a vulnerability, I care about a completely different class of sources. Vendor advisories, technical references, repo discussions, security data. The query might look like “search” either way, but the information problem isn’t remotely the same.

I’ve kept AnySearch in this setup because it sits between the agent and the information sources and can route different kinds of queries toward more appropriate domains instead of treating everything like the same generic web lookup.

I’m still verifying security-related information myself before acting on it. That’s not something I’m outsourcing to an agent.

But giving it a better starting point has been much less frustrating than watching it build a confident answer from whatever five webpages happened to rank.

Was trying to figure out if I could land at 8:40, catch the last train, and still make a hotel check-in cutoff.

sounds like one question until you realize the answer is sitting across airport info, train schedules and the hotel’s own policy.

I already had AnySearch connected to my agent, so I just gave it the whole thing instead of checking each part separately.

Still verified the train time before booking. I’m not gambling a night at the airport on an AI answer lol

Had to compare a few products for work and the first AI output looked fantastic.

It was also not very useful.

Beautiful prose, nice headings, lots of “X is ideal for teams while Y is better for flexibility” language. The actual differences I needed for a decision were buried underneath all of that.

I redid the workflow so AnySearch sat behind the agent as the search layer first, then asked the model to organize the information it received into a table.

Way better fit for the task. The agent had actual material to work from before it started writing.

I still ended up checking two pricing pages manually because those change constantly, but at least the first draft looked like something I could work with instead of a software review blog.

Fair criticism.

We’re a tiny team and every time our internal agent needed a new kind of information, my solution was basically “cool, I’ll connect another API.”

One for general web stuff. Another for a specific source. Then a scraper because one source didn’t return what we needed. Then another key because the free tier on something else was annoying. None of these decisions looked stupid individually.

Six weeks later we had a spreadsheet for API keys.

That was the point where my coworker asked why we were spending so much time maintaining the part that finds information instead of the thing we were actually building.

We’ve moved more of the general search work behind AnySearch since then. The useful part for us is that the agent gets one consistent entry point across different types of information instead of us wiring and normalizing every source separately.

But the spreadsheet is gone, so my coworker is unfortunately being unbearable about winning this argument.

I had a prompt folder that was getting embarrassingly large.

Every time I saw someone post a “perfect research prompt” or “best prompt for writing” I'd save it. Some were screenshots, some were giant markdown templates, some had 20 rules and three different roles defined before you even got to the actual task.

I kept thinking future me would be grateful.

Future me used almost none of them.

Most of the time the exact prompt didn't fit what I was doing anyway, so I'd spend longer editing the template than I would've spent just explaining the task from scratch.

A while ago I deleted most of the collection and kept a few really boring things instead: what am I trying to get, what context matters, what are the constraints, and what should the output look like.

That's basically my entire prompt library now.

Turns out I didn't need 60 clever prompts. I needed to get better at describing the actual problem.