Requido
Build AI Agents in Seconds — No Code, Full Workflow
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August 31, 2025
I built Requido this summer — and it changed how I work.
The problem: AI doesn’t know what’s in your head. If you don’t know what to ask or how to ask, you waste time fixing messy outputs.
The solution: I created Requido with two tools:
Promptex → turns vague ideas into expert prompts in seconds.
Agent Crafter → builds complete AI agents that work like a team (SEO, blog, email, ads, and more).
I’ve already built and used 20+ agents daily in my own work. It’s been a huge learning curve, but the result is clear: less trial-and-error, faster execution, and consistent quality.
I’m excited to finally share it — I think you’ll love trying it out.
👉 requido.com
15 Comments
Turning half-baked ideas into polished AI magic....that's genius
This is the main pain and the real value
This is really cool 👏
I’ve been following the rise of AI agents and I think the biggest challenge is exactly what you mentioned: messy prompts = messy outputs.
Quick question: do you see Requido as more of a personal productivity tool, or something that could scale into team-level workflows?
I’m building Kaelis AI (a proactive calendar agent), and I’m curious how others approach the transition from solving an individual pain to tackling collaborative use cases.
They are two sides of the same coin. The challenge is to make agents part of the day-to-day business. The current development of llm and agents is not specifically geared towards working collectively, but they will eventually become so. Therefore, the main thing now is to have tools that improve individual productivity (of an individual who may be part of a team) and, in addition, help to understand how they work.
That makes a lot of sense — I like the framing of individual productivity as the first step before team-wide adoption. I’m also wondering if once people trust these agents for their own work, the natural next step is to ‘connect’ them across teams.
Have you seen any early experiments where individual agents start collaborating (even in small ways)?
I have yet to see a consistent development based on a fully autonomous multi-agent system. Currently the big llm developers don't seem to be fully focused on this area (they have many), but it won't be long in coming, although it's hard to predict when. The main thing now is for everyone to create their own ecosystem of agents, fully customised and functional for each other, so that when multi-agent - beyond the general market - is developed with guarantees, it will allow professionals to automate decision making. But this is different from my post, as we see the first step as clear, and that is for everyone to (i) improve their decision making with clear time savings through the use of AI, with real value; (ii) learn how the agent system works to apply it to each subtask.
This is not difficult if you customise the creation of agents and once you have them created, you trust them. But this is different from a multi-agent system, without individual agents created first, making decisions that have not previously been validated as needed. It is important to give good instructions to the agent, with a well-defined structure that ensures an optimal, coherent, reliable and verifiable result.
Makes a lot of sense — especially the trust point.
From your perspective, do you see productivity tools (like scheduling/decision-making) as the best first playground, or will technical fields adopt faster?
The most optimal use should be technical, and by productivity we should not necessarily mean low-value or repetitive tasks, as this leads better to ultimate automation tools. For me, improving productivity through AI agents should be related to the creation of niche agents, i.e., specialised in a very specific technical field that support the professional to speed up their work, professionalise it and improve it.
That’s a great framing — focusing on niche, high-value agents feels like the right bridge between productivity and automation. Really appreciate your perspective 🙌 Curious to see how this evolves, especially as trust and adoption grow. Thanks for the thoughtful exchange!
It's good to share our views and try to help - doing our bit is already a success. It is good to consider these things and make them known. Thanks to you.
Building AI is not sufficient itself it need solid accuracy where people are failing
We agree. In order to really add value and increase productivity it is very important to communicate correctly to the AI. The assumption we make is that there is a training gap and that there are competencies, while doing it well takes time or a lot of things in the head.
What's a cool example of an agent that helped you in your day to day work?
In many ways, I have several and they are in development:
- one that acts as a ceo at a strategic level.
- one for legal consultations
- one for content creation
- one for copywriting
- one for copy
- one for advertising
If you have something in mind I can give you a demo of a specific one you can think of.
+1