Capacity Efficiency at Meta
Meta's Capacity Efficiency Program uses a unified AI agent platform to automate the detection and resolution of performance issues across its infrastructure. By encoding the expertise of senior engineers into reusable skills, the system has recovered hundreds of megawatts of power and reduced investigation times from hours to minutes. The platform supports both proactive optimization and regression mitigation, enabling scalability without proportional increases in engineering headcount.
- ▪Meta’s AI agent platform automates finding and fixing performance issues, recovering hundreds of megawatts of power.
- ▪The system reduces manual investigation time from ~10 hours to ~30 minutes and generates ready-to-review pull requests.
- ▪FBDetect, Meta’s in-house tool, catches thousands of regressions weekly, while AI agents handle growing volumes of optimization opportunities.
- ▪The platform uses standardized tool interfaces and encoded domain expertise to support both offensive and defensive efficiency efforts.
- ▪AI agents compress the path from identifying an issue to implementing a fix, enabling scalability across product areas without increasing team size.
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POSTED ON APRIL 16, 2026 TO DevInfra, ML Applications, Production Engineering Capacity Efficiency at Meta: How Unified AI Agents Optimize Performance at Hyperscale By Tommy Tran, Michael Zetune We’re sharing insights into Meta’s Capacity Efficiency Program, where we’ve built an AI agent platform that helps automate finding and fixing performance issues throughout our infrastructure. By leveraging encoded domain expertise across a unified, standardized tool interface these agents help save power and free up engineers’ time away from addressing performance issues to innovating on new products. We’ve built a unified AI agent platform that encodes the domain expertise of senior efficiency engineers into reusable, composable skills.
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