AI Agents for Performance: Ship Faster, Pay Less — Rajat Shah, Netflix
This talk outlines how Netflix leveraged AI agents to automate performance engineering, addressing the bottleneck of manually identifying and fixing inefficient code patterns in production services. The process involves feeding profiling data (call stacks, CPU usage) into an LLM agent, which identifies suboptimal algorithms (e.g., quadratic-time patterns). The agent then proposes a fix, validates it against a canary deployment using real production traffic, and can even scale the fix across multiple services by identifying common anti-patterns in a centralized catalog. This shifts performance optimization from a reactive, manual effort to a proactive, automated part of the SDLC.
Key takeaways
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The Performance Bottleneck
4:00
Traditional performance engineering is highly manual and time-consuming (taking minutes just to identify hot paths), making it difficult to scale across large codebases. This bottleneck increases as coding agents write code faster, potentially introducing inefficiencies.
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AI Agent Workflow for Optimization
12:17
An LLM agent can automate the entire loop: reading profiling data (call stack/CPU time), identifying anti-patterns (like O(N²) loops), locating the code path in the Git repo, proposing a fix, and validating it via canary deployment.
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Scaling Fixes with Pattern Catalogs
21:20
Instead of fixing one instance, agents can search across multiple services using a centralized 'Pattern/Anti-pattern Catalog' (stored in a Git repo) to find and propose fixes for the same bad pattern repeatedly implemented elsewhere.
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Shifting Left: Proactive Performance
26:40
The goal is to move performance improvement left in the SDLC. The catalog can be used proactively—at code review or even during code authoring—to suggest optimal patterns before suboptimal code reaches production.