Maybe, but using state-of-the-art large language models to solve customer support queries with agentic can quickly use a lot of tokens. What I understood from the talk is that they used agents with limited responsibility and (assumption from me) smaller models, to the make sure the answers were quick, reliable and not too costly.
There are several payments processing companies that are already largely using AI for customer support queries. They still have an escape hatch to a human but at least one of those companies (on the smaller side) is reporting a ~99% success rate, they are down to a handful of human customer service employees now for cases where the customer can't find/produce the transaction ID.
Oh wait, I just tested it on a higher speed (20) and I see what you mean. It's drawing over the existing Tetrominoes! I'm going to max the speed at 10.
The year is coming to an end, and looking back I had a lot of fun building quirky, fun, and insightful visualizations. Agentic coding agents have been hugely empowering for me. In 2024 it was “just” autocomplete on steroids. Now it feels more like conducting, directing the agents to go where I please. Often they’re impressive; sometimes they fail at the simplest tasks.
What worked particularly well here was a test-driven (TDD) approach. I asked Claude Code to focus only on the algorithm for selecting the next Tetromino (the Tetris blocks), starting with tests. After that, things went smoothly, though it still took many follow-up prompts (around 100) to reach the final result.
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