I just shared it to my marketing peers in the company and they find it useful to know
Hey, disclaimer first :: not really a marketing-experienced person here, using agents for sure in a different way. Yet my friend suggested that token costs is something anyone should be aware of.
Everyone eventually hit the usual wall "why isn't this more agentic yet" from leadership. Many people in the company asked me how to optimize it, should we have more tools, or one for all. I
Instead of just nodding along I put this together - kind of summary first - a map - where tokens can be optimised indeed. Sharing here too since I doubt we (engineering people) are the only ones getting that question with zero explanation of how.
If you hear discussions about token usage, it’s almost always about models, but in reality it's barely about which AI model you're using. It’s a small lever. One of the bigger ones is how much freedom you've given the agent, how much of a page or tool result gets shoved back into the model on every single step (the browser one's brutal - full page re-read after every click), whether old context actually clears instead of piling up, and whether repeat requests get cached instead of paid for twice.
I pulled real threads for anyone who would like to read how others addressed that depending on the stage in the agentic flow:
| What's eating tokens | Real thread | Where | The number |
|---|---|---|---|
| Which model you're on | My Codex model routing setup | r/codex | Routing by task |
| How much freedom the agent has | Cut tokens ~50% with one rule | r/codex | 465↑ |
| How much freedom the agent has | Caveman Claude | r/ClaudeAI | 13k↑, 75% claimed |
| What gets sent back per step | 67K tokens before I even asked a question | r/ClaudeAI | Measured, not estimated |
| What gets sent back per step | $377 a run down to $29 | r/mcp | 92% cut |
| Same thing, specifically for browsing | We cut browser-agent input tokens 66–80% | r/OperaNeon | Whitepaper, pass rate unchanged |
| Old context clearing properly | Compacted, still ran out | r/ClaudeCode | 219↑ |
| Old context clearing properly | “99% fewer tokens” claim, tested myself | r/ClaudeAI | Got 40%, not 99% |
| Repeat requests getting cached | 90% cost cut with caching | r/LLMDevs | Real, worth doing |
| Repeat requests getting cached | Storage cost more than it saved | r/GeminiAI | The catch |
Remember also, that none of this is solved-and-shipped anywhere, dev side included.
The thing is, the browser is where the most work happens right? So for such use cases, agents browse a lot. If they browse inefficiently, you pay for the noise… junk. I would focus on making sure your preferred AI is connected with the right CLI to the right browser.
Worth checking what your agent captures per page before assuming the model's the expensive part.
Open for some questions, if you want to share your agent stack for marketing workflows, based on browsing, or that use a browser as part of it.
Posted Jul 6, 2026, 1:26 PM UTC