Raw model capability has scaled dramatically. The dominant unsolved problem is orchestration under constraints. An open-ended task has to break into subtasks an agent can actually be matched to. The pieces run in parallel and come back together at production latency, and no one can afford a manual review on every piece.
Project Vercel is our long-term research infrastructure for this problem. We study online capability calibration, constrained placement under SLO objectives, minimal-sufficient context materialization, and reference-anchored validator quorum. The long-term target is a general-purpose coordination runtime for heterogeneous AI systems.
Benchmark-free online calibration of per-agent capability states. Vectors are updated continuously from task outcomes, validator verdicts, and telemetry. No held-out test sets required.
Constrained approximation for dynamic subtask placement under latency, cost, and SLO objectives. The current focus is bounded-ratio greedy placement with incremental rebalancing.
Learned minimal-sufficient context views under dependency closure. The goal is the smallest projection that still preserves cross-subtask coherence.
Reference-anchored validator quorum and adjudication for distributed inference outputs. Combines semantic divergence detection with hard constraint checks and executable validators.