Kopai
The Cloud for AI Agents
Start new thread

What are the immediate AI Agents/Tools that you would want that will help you?

by
•

In this time when we use LLM's for basically anything, what AI agents/tools would you want that you would actually pay for?
What are you spending a lot of money on and would probably save a lot of you time if you got the Agent out of box, or if configuration was much easier?

373 views

Add a comment

Replies

Best
something that handles context better & between sessions, i feel like im always telling agents what to store in markdowns for future agents to reference. There should be some framework for agents to manage context automatically without losing model quality. I know a lot of tools like claude code auto compress at a certain point and store internal markdowns but it can be a bit rough around the edges and i feel like it causes model quality to drop a good bit over time

 Yep, that seems like its exacty what I was thinking of, thanks!

 Glad it helped! Feel free to reach out if you run into anything else while trying it out.

 At kopai, we are actually working quite heavily on context specifically in between sessions. We also have a feature called Chat Orientation which helps users and the agent understand the goals and actions taken and change it if you feel that the agent is drifting.

Would love if you could check out () and give us your review so that we can work on it.

looks really interesting, I understood it as a marketplace of agents that creators can make quickly, give it documentation ect and agent gets a vector db to reference if that sounds correct?. Can you give ur agents different tools aswell?

 Yes. You can give it different tools, websearch , knowledge base(the RAG) and also subagents (Other agents in the marketplace). Every agent needs to go through an eval system before it can be published.When a user chats with your agent it automatically has inbuilt features like GEN UI, Document creation, Image generation(depending on the model chosen) file ingestion(pdf, doc) and also has a memory and context layer inbuilt. each chat has inbuilt chat orientation which helps keep the user and agent towards a similar goal.
Finally the more number of users use your agent, the more you earn from it

What I want isn't more agents, it's fewer places where I still have to double check its work before trusting the output.

 What if you had some kind of guarantee, like an evaluation system that you can trust? Would that help?

An AI researcher that compares products using docs, Reddit, GitHub, and real reviews would be incredibly useful. Have you found anything close?

 Not really.
Disclaimer, a little self promotion here but I made a product that would solve this exact problem. Maybe if you are up for it you can go make your own AI agent on (). It has all the connectors, it is no code and you can publish it as well for other people to make it. All under 10 mins.
Do try it out

  That actually sounds pretty close to what I had in mind. I’d be especially curious to see how well it handles messy sources like Reddit and GitHub though, rather than just pulling from clean docs. Might give it a try.

 hey Jeremy, I took the liberty of building an agent on Kopai, by stealing your idea, haha! Check this chat out -

Here's the agent I built -

Tried comparing Linear and Clickup here. Let me know your thoughts! You can try building agents yourself too!

For me, the AI tools worth paying for are the ones that feel safe to use in real work, not just impressive in demos. That’s why I’m building Verlox: plan first, approval before action, full visibility, and an undo path when something goes wrong. The best agents won’t just be smart, they’ll be accountable.

The one I'd actually pay for: something that closes the loop between AI output and human review, instead of treating a generated result as final.

Most agent tools I've tried are confident about everything same tone whether it nailed the task or quietly guessed wrong. What I want is the opposite: an agent that's explicit about which parts of its output it's sure of and which parts are a best guess, so I know exactly where to spend my own attention instead of re-checking everything or trusting all of it blindly.

Closest I've seen this matter in practice: anything doing structured judgment work grading, categorizing, extracting data from messy input. The moment a tool has to make a call rather than just generate text, "I did this, trust me" stops being good enough. Configuration isn't really the blocker for me; trust calibration is.

Curious whether others are running into the same thing, or if this is specific to the kind of work I do.

Not an agent. A memory layer that any of them can write to and read from, that survives the session and is not owned by one vendor. Right now every tool keeps its own context, so the same constraint gets re-explained to each of them, and none of them knows what the others already learned. The moment you use two, you are the integration. The reason nobody has built it is that it is unglamorous and it only pays off across tools, which means no single tool has the incentive. The one I would actually pay for tomorrow: something that watches what I told an agent, notices when I contradict it a week later, and asks which version is current. Not storage. The contradiction detection is the product.

 We actually have a group chat feature, which means you can have two or more different agents owned by different vendors to consult each other( with shared context) so that they can reply to you. Would love if you could try out the feature and let us know what went right and what went wrong