How browser-agent bookkeeping can slash AI costs

Asana optimized its browser agent’s caching and screenshot handling, then released the browser-navigation changes in StackAI, according to OpenAI’s account. In a 144-run study, the optimized workflow on GPT‑6.1 Sol averaged $0.47 in estimated model costs and roughly four minutes per run, compared with at least $36.21 and 22.5 minutes for the original setup on Model B. [7] Why it matters: The results suggest agent economics depend on how software manages accumulated browsing context, not just which model it selects. On GPT‑6.1 Sol alone, changing the caching and screenshot policy reduced estimated cost from $1.97 to $0.47 per run, although the evidence comes from a vendor-published study using one catalog task. [7] Key insights: The original agent cached fixed instructions and tool definitions, but repeatedly resent its growing page-text and screenshot history at full price. [7] | The optimized policy let screenshots accumulate to 20 before retaining only the latest one, keeping earlier history unchanged for longer stretches and improving cache use. [7] | In the optimized GPT‑6.1 Sol workflow, 89% of input came from cache, priced at 5% of uncached input; each call was about three times cheaper. [7] | The headline 76-fold saving combines workflow changes and a model change. On Model B alone, optimization reduced estimated cost to $1.24, a 29-fold reduction. [7] Cheatsheet facts: What changed: Asana deployed revised browser-history caching and screenshot handling in StackAI. [7] | Why now: An investigation found that growing browsing history was being resent at full price; the study tested alternatives across four models. [7] | Watch next: Repeatable comparisons of cost, runtime and answer quality: Asana says it is developing tools to bring these experiments into platform evaluations. [7]