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AI Token Economics: How to Cut AI Agent Costs Without Losing Quality
Token economics works like cloud cost optimisation: the principles are the same, only the unit of spend is new
What is AI token economics?
AI token economics, also called tokenomics, is the practice of getting the most out of every token your AI agents spend, in the same way cloud cost optimisation gets the most out of every euro spent on cloud. Tokens here are the units an AI model reads and writes, not crypto tokens, and the tokens it spends "thinking" are paid for too.
You cut AI agent costs without losing quality by measuring two things together: what your agents actually do, and how well they do it. With both in place, you can test a cheaper model, a lower thinking level, a tighter prompt or better tooling, and keep only the changes that hold quality.
Why token costs matter now
AI token economics matters now because adoption has outrun the ability to prove value. In the Data & AI Monitor 2026/27, you can see that 71% of surveyed organisations work with GenAI assistants (63% in 2025), and 28% already run AI agents in production or scaled across multiple processes. Yet only 33% say they fully harvest and track the value AI brings, the same share as a year earlier, and 16% do not measure AI business value at all.
The report's reading is that strategy, risk awareness and tool adoption have moved forward together, while value realisation stood still. For AI token economics, that gap is the problem: every new assistant and agent adds token spend, and if you cannot measure what that spend buys, you cannot tell useful use from waste.
Two ways to make AI cheaper: change the model, or change what the agent does
Token economics works like cloud cost optimisation: the principles are the same, only the unit of spend is new. There are two directions.
- Consider alternatives to the premium product. Move from a frontier foundation model to an open-source model you host yourself, which is cheaper to run. This direction can give you quick results without needing to make any changes to the agent itself.
- Look at what your agents are doing. Are they making mistakes? Are they spending tokens on steps that do not lead to a correct result? This direction requires changes to the agent, but not the underlying infrastructure the agent is running on.
The rest of this post will deepdive into these directions.
You can't optimise what you can't see or score
Two instruments make token costs optimisable, and they should always be used together.
- Visibility, through an AI gateway. A proxy between your agents and the LLM provider that captures what they do: tool calls, retries, thinking and the number of steps to reach a goal. Without it, an agent is a black box and you only learn whether it succeeded. With it, you can spot waste, such as every agent writing its own script for the same simple task.
- Score, through a golden data set. A set of example inputs with the correct, human-checked outputs, like the test set in traditional data science. It shows how well an agent performs, so you can check whether a cheaper setting still holds quality.
Visibility alone cannot tell you whether a cheaper setting is good enough, and a score alone cannot tell you where the tokens go. Cost on one axis and score on the other is what lets you choose a setting with confidence.
AI Token Economics: Four knobs you can turn to reduce spend
Once you can see and score your agents, four settings do most of the work. The traces tell you which one to turn.
| Knob | What you change | How you know it worked |
|---|---|---|
| Model | Switch from a premium foundation model to a cheaper or open-source one | The golden data set score stays the same |
| Thinking level | Lower (or raise) how much the model reasons before it answers | Score compared with cost at each level |
| Prompt | Make instructions clearer; an unclear prompt can send an agent in the wrong direction | The trace shows what the agent actually does with the prompt |
| Tooling | Give agents a shared, tested tool instead of letting each one rebuild it | The trace shows fewer repeated steps |
Why a cheaper model does not always mean a cheaper bill
Price per token is only half of the cost of a task. The other half is how many tokens the model needs to reach a right answer. Thinking is tokens too, so a higher thinking level means a higher cost and higher latency, for gains that get smaller at each step. A cheaper model may need many more tokens than a stronger one, and an expensive model at a low thinking level can beat a cheap model at a high one. You cannot know up front, so test it on your own task.
In our own usage data, Haiku, a much cheaper model than Sonnet, used many more tokens than Sonnet, which narrowed the price gap by a lot. Haiku is cheaper per token, but not by as much per task.
Not every task requires a lot of thinking
Thinking levels show diminishing returns in public benchmarks. In the Artificial Analysis Intelligence Index, going from xhigh to max adds only about 0.2 index points for GPT-6 Astra, at roughly 40% more cost per task (figures approximate). And this aligns with everyday life, making a coffee requires much less thinking than solving a complex math problem. The same is true for agents, but you need to tell it explicitly beforehand how much effort/tokens it should spend on a problem.
A clear prompt will reduce the number of mistakes
Similar to a manual, a clear prompt will reduce the number of mistakes an agent makes. Especially true in tool usage, clear instructions will allow the agent to use a tool in the first try instead of guessing how a tool should be used and failing. Looking at traces of an agent will allow you to detect/optimize this and reduce the tokens spend.
Optimize tooling for the task assigned to the agent
Custom tools can also reduce the tokens spend, if an agent needs to find information or interact with external systems, custom tooling can reduce the tokens spend on completing those tasks. As an example, if an agent needs to interact with an api, then it might build a custom throwaway script to do so, providing one by default removes the effort/tokens required to build this tool over and over again.
How to start: instrument, analyse, advise, measure
The same loop works whether you run autonomous agents or give developers AI assistants:
- Instrument. Capture every session: model, tokens and tool calls. Where privacy matters, stream only metrics.
- Analyse. Look for patterns that cost money without improving output, such as a frontier model on a trivial task, growing context, repeated retries or sessions left open.
- Advise. Give each team or developer a short, personal list of costed actions, with the evidence behind them.
- Measure. Check next week's data to see which advice stuck, so savings are shown rather than claimed.
Before you move a task to a smaller model or a lower thinking level, check the change against your golden data set.
Conclusion: two instruments, four knobs
AI Token economics comes down to a combination. The AI gateway gives you insight into the black box of how an agent reaches its result. The golden data set gives you the score. With both, you can turn the knobs (model, thinking level, prompt and tooling) and choose the setting that gives the best result for the money. Without the score, you have no way to say which setting is best.
Want better visibility into token consumption and the cost of running AI at scale? Xebia's Data & AI experts can help you establish the right metrics, identify optimization opportunities, and manage AI costs as adoption grows. Get in touch with our team to discuss your challenges.
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