Lightning Talk - The Plan That Learns#

Ben Vallis (Meta)

• Why: Capacity planning is where years-ahead commitments meet guaranteed-wrong forecasts, and this talk applies agents not to forecast accuracy but to decision quality under uncertainty — positioning capacity to keep options open — which is a more defensible use of ML than "better predictions." Core to the PM's ML-for-infrastructure-operations interest.
• What: Agents applied to the judgment-heavy decisions that stayed manual: acting inside narrow buy windows (continuous signal triage surfacing the handful of truly novel sites, weeks down to minutes); trusting data across systems that disagree on basic definitions (a measure-enrich-regrade loop); and choosing among coupled, timing-sensitive levers (a ranked, costed menu of moves with the reasoning attached). The stated frontier: agents that remember every decision and its outcome, so judgment compounds instead of resetting with each planner. (from abstract only)
• How: Agentic triage over continuous signal streams, a cross-system data-trust reconciliation loop, and decision memory — explicitly framed as buying optionality under wrong forecasts, not improving the forecast.
• Where applicable: Meta's capacity planning (years-ahead commitments, correlated demand shocks) — org-specific context and lightning-talk-stage maturity, but the optionality framing and decision-memory idea transfer to any large operator. The companion talk AI-Native Network Capacity Management covers the same lifecycle pipeline stage-by-stage with agents as first-class participants.