Running multiple AI Agents without burning credit
Today was one of those deeply focused, high-output days where everything revolved around AI. I spent the day running multiple AI agents simultaneously, managing several parallel projects. Some of these are real products we’re currently developing with the team, while others are experimental side projects that I like to build when I need a creative break.
The funny part is that my initial goal was quite simple: use up the leftover AI credits I still have for the month. Yet, even after an entire day of tinkering, I’ve only managed to reach about 5% of my available usage, with just a few hours left before the billing cycle resets.
While many people I talk to either burn through their AI credits too fast or struggle to make meaningful progress with them, I seem to have the opposite problem . Efficiency in working with AI agents comes down to clarity and control.
To make the most of your credits, you need to know exactly what you’re trying to achieve, choose the right model for each task, and feed it the right context with precise instructions. When your prompts are clear and your objectives well-defined, you spend fewer tokens and get results faster.
It’s not about working harder or feeding more data into the system , it’s about communicating with your AI like you would with a skilled collaborator. Clearly, respectfully, and with enough context to let it do its best work.
If you’ve been facing the challenge of burning through your credits too quickly or not getting the value you expect, I’d be happy to chat.
Sometimes, a small adjustment in how you frame your problem can make a huge difference in performance and cost efficiency.