Token cost is the tax nobody budgets for until the invoice shows up.
A tool called
@HeadRoom has been climbing GitHub trending this week β it sits between your agent and the model and strips out context waste before it ever reaches the LLM. Its own numbers: ~92% fewer tokens on SRE-style incident debugging, ~73% on issue triage, ~92% on code search.
β οΈ flag before repeating those numbers as fact: they're self-reported by the project on specific workloads, and I haven't seen an independent benchmark yet. Also worth confirming β some coverage nicknames it "the Netflix tool," but I couldn't verify an official Netflix affiliation, so I'd treat that framing as unconfirmed.
What I can vouch for from building a multi-provider LLM gateway myself: context bloat is the single biggest lever on both latency and cost, bigger than model choice most of the time. Every agent session that re-reads a whole file to change one line, every tool call that dumps a full log instead of the relevant slice β that's money and seconds, compounding across thousands of calls a day.
If you're running agents at scale and haven't audited what's actually going into your context window, that's the highest-leverage afternoon you can spend this week.
What's the worst context-bloat mistake you've caught in your own agent setup?
#AIagents #LLM #DeveloperTools #Engineering
