Most of your token spend goes on your agents rediscovering the same things.
AGENTS IN PRODUCTION, AND A TOKEN BILL THAT GROWS WITH USAGE
Your agents work against your own systems, and every request begins by reconstructing context that was reconstructed a thousand times before.
Adding tokens stopped improving the answers a while ago. It kept increasing the bill.
The cost of understanding your system is incurred per request, per agent, forever.
The number that matters is what one completed customer result costs, and it is worse than the per-call price suggests.
More context stopped helping, which means the problem is not the amount.
What does this agent need to know to complete this task?
Where are we paying AI to understand the same thing again?
How do we reduce cost per successful customer outcome?
Which parts of our context budget are actually contributing to the answer?
The agent asks why a customer could be charged twice.
It receives the paths through the product that produce that outcome, with the evidence, instead of a retrieved pile of payment code.
It resolves the case against what the product does rather than against what the snippets implied.
Fewer tokens per case and fewer cases escalated back to a human because the answer was wrong.
A WORKED EXAMPLE OF HOW THE WORK GOES, NOT A CUSTOMER CASE STUDY. WHEN WE PUBLISH A CUSTOMER RESULT IT WILL BE MEASURED AND NAMED.
The knowledge is not re-derived per request, which is the line item that scales with your usage.
On the published benchmark, the same model answered better on a third of the context, at a third of the cost.
The intelligence is not baked into weights, so a new model inherits it.
Multiple projects, company sources and higher usage. See also ensemble for production agent workloads.
OTHER COMPANY TYPES
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