Braintrust CEO reveals how AI agents handle technical debt and database optimization
The Gist
- Ankur Goyal uses Codex to run week-long benchmark experiments across database indexes
- Agents can now handle deeply technical architecture work no single human could tackle
- Evals are the new PRD: encode 'what good looks like' so models figure out the 'how'
Key Quotes
Evals are the modern version of a PRD.
There’s no excuse to skip rigorous benchmarking now that agents can run them tirelessly.
Key Insights
- AI agents can handle deeply technical architecture and infrastructure work that no single human engineer could tackle before.
- Evals are the modern version of a PRD (Product Requirements Document) and help encode 'what good looks like' so a model can figure out the 'how'.
- There’s no excuse to skip rigorous benchmarking now that AI agents can run them tirelessly.
- The 'agent line' framework helps decide which decisions, directions, and interactions can be handed off to an agent.
- Fixing your CI (Continuous Integration) is the highest-leverage way to speed up engineering velocity.
- Human attention decays on tedious work, making AI agents ideal for repetitive or monotonous tasks.
Actionable Takeaways
- Implement AI agents to handle repetitive or complex technical tasks like database optimization and benchmarking.
- Use evals to define and scale quality standards in AI-driven projects, replacing traditional PRDs.
- Invest in CI/CD infrastructure to maximize the efficiency of AI-accelerated engineering teams.
- Adopt the 'agent line' framework to systematically delegate tasks to AI agents based on their capabilities.
Data Points
- 4 to 6 concurrent agents (Ankur Goyal's workflow involves running 4 to 6 concurrent AI agents for various tasks.)
RevBots.ai View:
Engineering teams at the ARM stage are using AI agents not just for coding but for system-level optimization previously impossible at human scale.
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