How Meta is scaling senior engineering knowledge

In a large system like Meta's, minor inefficiencies can really add up. I synced with Tommy Tran, software engineer at Meta, to learn how agentic AI is helping here.

Meta is now deploying AI agents in its capacity efficiency efforts to reduce power use. They're recovering hundreds of megawatts of power and slashing detection and remediation time for performance regressions.

Part of that, which my article emphasizes, is translating the knowledge of senior engineers with deep expertise into reusable skills.

"The core idea I set out to prove was that you could take the judgment those engineers apply, encode it into a platform, and make it scale," says Tran. "That is what I built."

To be clear, this isn't about replacing engineers. You still need them to evolve those skills over time. It's about unlocking tribal knowledge and scaling it through an automated process that would be nearly impossible to replicate manually.

It's an interesting example of using AI agents to make things more efficient! Plus, case studies correlating AI use with a direct business ROI are surprisingly rare, but this one checks out.

Check it out on LeadDev for a look at the process, how it started, and takeaways for anyone building agentic systems: https://lnkd.in/gBd-bxpG

Thanks so much to Tommy Tran and the Meta comms team for syncing us up!