Building a Rust Multi-Paxos Engine with AI: Lessons from 130K Lines of Code
By
pramodbiligiri
11d ago· 8 min readenInsight
100/100
Golden Brown
Bagelometer↗
Baker's choice. Dense with flavour, light on filler.
Score100TypeanalysisSentimentpositive
Summary
A developer shares their experience building a 130K-line Rust-based multi-Paxos consensus engine using AI coding agents over ~3 months. The project modernizes Azure's Replicated State Library (RSL) by adding pipelining and NVM support, achieving a performance boost from 23K to 300K ops/sec. Key learnings include using AI-generated code contracts for correctness, lightweight spec-driven development, and iterative AI-assisted performance optimization. The author also provides a wish list for future AI-assisted coding improvements.
Key quotes
· 5 pulledThe real breakthrough came from AI-driven code contracts. Code contracts specify preconditions, postconditions, and invariants for critical functions.
Performance optimization is where AI really shines. After ensuring initial correctness, I spent about three weeks purely on throughput tuning — and AI became my co-pilot in performance engineering.
I've found that coding from the CLI creates a perfect asynchronous flow that maximizes my productivity.
That single contract saved what could have been a serious replication consistency issue — well before it ever hits production.
Each improvement felt like peeling another layer of latency off a high-performance engine — without fear of corrupting memory.
In the past few months, I’ve been stress-testing how far AI coding agents can take us when building real, production-grade distributed systems. The result: a Rust-based multi-Paxos consensus engine that not only implements all the features of Azure’s Repl
