The Unbearable Lightness of Agent Optimization — Alberto Romero, Jointly
Nov 24, 2025 · 17:58
Alberto Romero, co-founder and CTO of Jointly, introduces Meta-ACE, a meta-optimization framework that orchestrates multiple adaptation strategies to overcome the limitations of single-dimensional context engineering like ACE. The framework uses a meta-controller to profile task complexity, uncertainty, verifiability, and resource constraints, then allocates strategies across context, compute, verification, memory, and parameter dimensions. Initial results show 8-11% improvement on agent benchmarks, 30-40% reduction in compute costs, and 6-8% gains on domain-specific tasks. Meta-ACE addresses ACE's weak reflector problem with quality gates and multi-signal reflection, feedback brittleness via a hierarchical verification cascade (self-verification, multimodal consensus, execution checks), and task complexity mismatch by dynamically adjusting strategy allocation to save up to 90% compute on simple tasks. Future work includes scaling to multimodal and compound AI systems, with challenges in meta-controller training stability, computational overhead, and verification cascade brittleness.
