Liquid AI Reveals 'Loops' Architecture for Production-Grade Coding Agents
JosephJacks_ · x · 2026-08-19
Liquid AI's engineering blog details how they designed "loops" to enable coding agents to autonomously solve production-grade problems.
Context & Challenge: The team needed a BPE tokenizer trainer capable of processing trillions of tokens on a single machine. Existing tools like sentencepiece, Hugging Face tokenizers, and tiktoken failed to meet performance or feature requirements (e.g., OOM errors, lack of warm start, and per-language vocabulary budgets).
Experiment:
- Phase 1: Two agents using the best publicly available coding models built a working toy trainer in 30 minutes but failed to scale to production.
- Phase 2: Introduced loops, allowing agents to iterate against real production data at scale. Correctness was verified using two independent libraries that the agent could not manipulate.
Result: The production-grade tokenizer trainer toktoktok was developed and open-sourced. The post discusses how to specify goals for multi-domain experts and set up verification infrastructure.
Related event: Liquid AI Reveals How Loops Architecture Powers Production Coding Agents(2 posts)→
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