Reasoning tasks are more expensive than CPU tasks. To efficiently spend tokens, you should minimize reasoning by using external resources to store necessary and contextually relevant data.
There are an increasing number of posts about people spending an obscene amount of tokens to do normal tasks, among them Amazon check-outs, or cumulative health tracking. And on the other side, is also the growing number of people who are #tokenmaxxing as they work to uncover the next big breakthrough. Whichever camp you find yourself in, there is an easy, simple way to think about maximizing tokens and reducing spend.
TLDR: Reasoning tasks are more expensive than CPU tasks. To efficiently spend tokens, you should minimize reasoning by using external resources to store necessary and contextually relevant data.
You use the largest amount of tokens when you are asking your agent to do reasoning skills. This means that every time you ask your agent to compare, predict, understand or connect the dots, you are paying tokens.
The simple workaround for this is to reduce the number of reasoning functions your agent is required to do. One way to do this is by increasing the memory agents have between conversations i.e. if it remembers what happened in the past, less time is spent re-informing the agent and then it spends less time replicating the logic in previous sessions. This is why there is a memory race at the moment. But, tools like Athropic’s Agent Memory are going to make this easier across the board.
But, there is another way to reduce memory burden and that is by training your agent to access information that is important to you (health data, calendar, email, etc) without also needing to store all that memory. Storing everything about you in an agent’s conversation memory takes more tokens than housing your critical real-world and personal data elsewhere, and training your agent to query that data.
Therefore, the most efficient way to maximize tokens is to build your agent infrastructure with a connection to an outside source that houses all of your relevant human data. This is where Fulcra comes in. Fulcra houses the data about the real world state of a human’s life: your favorite Apple TV shows, your calendar, your Apple Health data, and your grocery lists, and lets your agent (whether on OpenClaw, Claude or any MCP) query it.
Before a typical agent-human experience looks like:
"Hey, I didn't sleep well last night — got about 5.5 hours, HRV was 38, I have three meetings today including a tough one at 2pm, I'm working from home, no workout planned. Given that, can you..."
This means you are spending ~120 tokens of re-reasoning surface before the actual question. The agent has no stable place to look, so you stuff the answers into the prompt. If you’re doing this daily, it’s going to get expensive.
After building a real-world state with a contextual memory:
"Hey, what should I prioritize today?"
The agent makes one scoped Fulcra query that covers any real world data points like sleep, recovery, calendar, location and reasons over the result in ~150 tokens. The query itself runs on CPU. The reasoning runs once and is new every time but applied to today's actual data, not the pasted-in reminder.
In essence, Fulcra holds the data so that your agent does the reasoning. And, this recipe can now be applied to every cron job, every Slack reply, every morning brief, every workout suggestion, and every workflow that has to start by re-establishing where you are and what's going on.
Fulcra adds normalization across 200+ data streams like Whoop, Oura, Garmin, Apple Health, Dexcom, Libre, Google Calendar, location, meals, weather, environment all of which is behind one auth boundary with scoped permissions and a full audit log.
Part of the simplicity of the infrastructure is that it is organized by time. This allows agents to find information effectively and efficiently. Your agent doesn't have to search everywhere to get the data; instead, it can search Fulcra, by time, which dramatically reduces bloat.
Fulcra is free at the individual tier. Scoped permissions. Full audit log.
