Infra

Benchmarking AI Agents on Full-Stack Coding

Martin Casado and Sujay Jayakar Posted March 28, 2025
In this episode of the AI + a16z podcast, a16z General Partner Martin Casado sits down with Sujay Jayakar, cofounder and Chief Scientist at Convex, to talk about his team’s latest work benchmarking AI agents on full-stack coding tasks. From designing Fullstack-Bench to the quirks of agent behavior, the two dig in to what’s actually hard about autonomous software development, and why robust evals — and guardrails like type safety — matter more than ever. They also get tactical: which models perform best for real-world app building? How should developers think about trajectory management and variance across runs? And what changes when you treat your toolchain like part of the prompt? Whether you’re a hobbyist developer or building the next generation of AI-powered devtools, Sujay’s systems-level insights are not to be missed.
Drawing from Sujay’s work developing the Fullstack-Bench, they cover:
  • Why full-stack coding is still a frontier task for autonomous agents
  • How type safety and other “guardrails” can significantly reduce variance and failure
  • What makes a good eval — and why evals might matter more than clever prompts
  • How different models perform on real-world app-building tasks (and what to watch out for)
  • Why your toolchain might be the most underrated part of the prompt
  • And what all of this means for devs — from hobbyists to infra teams building with AI in the loop
AI + a16z

Artificial intelligence is changing everything from art to enterprise IT, and a16z is watching all of it with a close eye. This podcast features discussions with leading AI engineers, founders, and experts, as well as our general partners, about where the technology and industry are heading.

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