Muhammad Umar uses AI agents on top of a CMS and project management tools to automate product and sales reporting. The quality of what the agent produces depends entirely on the quality of the prompt and context you give it. Good prompts generate useful reports; bad ones produce nonsense.
So for example, I have a CMS and I have an agent on top of it. Then I use different engines for project management that we build on top of our tools. So I use a lot of agents. So, for example, think about that. Every day I'm leading a product and every day I just extract a report from the agent that traditionally had to assemble from just looking at the dashboard and lots of inference based on that. And that made me harder to do it every day. So I used to do it, let's say twice, once in two weeks, but now I do it daily. But the way the adaption comes in, when it's so easy to extract that code through that assistant, which works with the voice, which works with natural language, I use lots of voice AI. And then. So agents are helping me a lot there also in sales, my sales agents, they are helping me a lot. But the challenge what I find is, I mean, when you ask. So the way I use agents is like I always. So once the agents are there, once you have developed, the agents always work with the ChatGPT or Claude, whatever LLM you're using for general purpose. So I asked them that give me a prompt for this agent and I try to give it a context. I give that context and then it extracts me the report. I create a prompt from a different language model and then I use it on an agent. Now the better job I do in giving the context and preparing a prompt, the better the results are. Sometimes it gives me the reports of holy shit, I haven't thought about it. And sometimes it's like how to make it think I don't need this. I mean, this is bullshit crap. And.
FDE Epi : 16 | AI Agents, Billion-Dollar Infrastructure & the Future of Intelligence
