I've landed on a tiered approach after running ~10 autonomous agents daily on a large static site project.
For scheduled background tasks — things like checking Search Console data, auditing pages for broken links, scraping trending topics — speed wins every time. These agents run unattended, so the limiting factor is throughput, not brilliance. A fast model that can follow a structured prompt and call the right tools is plenty.
For anything that touches content quality or architectural decisions — writing analysis copy, deciding which pages to prune, evaluating SEO strategies — I want the smartest model I can get. The cost of a subtle wrong decision compounds across thousands of pages.
The interesting middle ground is agents that need to react to what they find. Like an agent that audits a site and needs to decide whether a data anomaly is a real bug or just noise. That judgment call is where smarts pay for themselves — a fast model would just file the ticket either way, creating noise.
Harsh's framing of "reversibility" is spot on. I'd add another axis: blast radius. If the agent's output affects 1 file, speed is fine. If it affects 10,000 pages, give me the smartest model and I'll go make coffee.
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I've landed on a tiered approach after running ~10 autonomous agents daily on a large static site project.
For scheduled background tasks — things like checking Search Console data, auditing pages for broken links, scraping trending topics — speed wins every time. These agents run unattended, so the limiting factor is throughput, not brilliance. A fast model that can follow a structured prompt and call the right tools is plenty.
For anything that touches content quality or architectural decisions — writing analysis copy, deciding which pages to prune, evaluating SEO strategies — I want the smartest model I can get. The cost of a subtle wrong decision compounds across thousands of pages.
The interesting middle ground is agents that need to react to what they find. Like an agent that audits a site and needs to decide whether a data anomaly is a real bug or just noise. That judgment call is where smarts pay for themselves — a fast model would just file the ticket either way, creating noise.
Harsh's framing of "reversibility" is spot on. I'd add another axis: blast radius. If the agent's output affects 1 file, speed is fine. If it affects 10,000 pages, give me the smartest model and I'll go make coffee.