> I have no idea what people are doing to burn so many tokens
Agentic workflows is what consumes a lot. When you have an automated agentic loop working towards a given goal. If you use an LLM as a support for your own work you don’t end up consuming that much tokens, if you have multiple agents working on things independently, reviewing the work of other agents, etc you very, very quickly burn all your budget
I learned that running goal for hours produces exponentially more slop than running targeted prompts over and over again manually.
Personally, I use gpt 5.5 high with planning every time and plan various smaller features/changes in parallel, then approve them one after another. This allows me to steer it (which I need more often than not) before approving the plan, thus reducing the otherwise accumulating slop.
Using goal doesn't work for everyone, unless you have an unreasonably strong test suite or harness that the agent can verify against.
Like a junior engineer, I find the models to be too ambitious and unable to steer themselves at a high level yet. What I’ve done to address this is prompting the model to break down its plans into more atomic steps. For whatever reason, they’re still lazy at planning.
Agentic workflows is what consumes a lot. When you have an automated agentic loop working towards a given goal. If you use an LLM as a support for your own work you don’t end up consuming that much tokens, if you have multiple agents working on things independently, reviewing the work of other agents, etc you very, very quickly burn all your budget