Post

At Quesma we are researching the economics of AI agents: what agentic coding really costs and what you can do about it. For this research I am running my own deep research setup, a pipeline of agents that builds a knowledge base I can actually trust. The first version of this setup burned the whole limit of my Claude Max 5x plan in 30 minutes. This post is the story of how I fixed the cost and the trust, using only subscriptions I already pay for, and how you can build the same.

My goal was to understand the entire state of so-called tokenomics. I wanted to know what monitoring systems exist, how teams govern their AI spend, and which optimization tools and practices actually work, both in the papers and in the real world.

I started the usual way, with /deep-research . I gave it the big open question and let it run. After around 30 minutes of research, I hit a limit and had to wait a few hours for it to reset. And I had no results. The run launched 111 agents and queued 123 claims for verification, but only 25 got verified before the limit hit, and the final synthesis never ran.

So, it hit me personally. And this is a bit funny: from the very first day I had to optimize tokens while still discovering how to optimize tokens. Learning by doing.

Using every subscription I already pay for

If Claude Fable 5 is out after 30 minutes and /deep-research is consuming so many tokens without giving me any results, what can I do to make the research more effective? I started thinking about what kind of tools I already have and pay for. Claude, Codex, and Antigravity: 3 subscriptions, and in theory 3 times more tokens without paying anything extra. What if I use all of these tools together, with shared memory?