By skyforbes 
It requires problem solving mindset.
This isn’t theory-posting it’s the exact approach I’ve repeated to get things done with AI Agents.
Start with a tiny, crystal-clear problem:
Skip the idea of building a “super agent” right away. Pick one job that’s narrowly defined and practical. Think along the lines of:
– Reserving a dentist appointment from a website
– Scanning job platforms and sending relevant listings
– Turning unread email threads into short summaries
The more specific the task, the easier it is to architect and troubleshoot
Pick a solid Tool to Automate Tasks.
Don’t bother training a model from scratch in the beginning that’s a distraction. Use something reliable out of the box. BhindiAI, ChatGpt Agent, Energent, The key requirement: tool must be to connect with multiple Tools & using the Best LLM underneath to get that done. Should be easy to Connect Apps & not too complex to get things done.
Build the core loop
Don’t dive into complicated task yet. Wire up the basic flow:
– Take a goal or command
– Give instruction/prompt to the Tool
– Use tools that Auto Adds the Tools that is needed to get things done.
– then it takes an action (API call, scrape, file read), & executes it
– Then it gives out your desired response
This cycle — Prompt→ action → output — is the backbone of every agent
Add memory slowly
A lot of people assume agents need advanced memory systems from day one. Not true. Start with the simplest form: short-term context (recent messages). If long-term recall becomes necessary, use a text file that is your small database.
The fastest way to really understand agent development is to complete one end-to-end. Once you’ve done that, every future agent becomes dramatically easier — because you understand the full lifecycle.