Holistic Agent Leaderboard + AgentAtlas papers identify the evaluation-infrastructure gap — what the field needs to build through 2027

Single-benchmark leaderboards can't capture multi-dimensional capability profiles. Outcome-only metrics miss process-quality information. Reward-hacking attacks compromise individual benchmarks. The Holistic Agent Leaderboard and AgentAtlas papers identify what the field needs to build for the H2 2026 to 2027 evaluation-infrastructure direction.

The Holistic Agent Leaderboard paper and AgentAtlas's process-and-outcome integrated framework together identify the evaluation-infrastructure gap that single-benchmark leaderboards leave open. The H1 2026 six-benchmark consolidation gave procurement teams a shared vocabulary; the H2 2026 to 2027 direction needs to build the integrated infrastructure around that vocabulary.

The three infrastructure requirements

Cross-benchmark holistic evaluation (Holistic Agent Leaderboard direction) addresses the multi-dimensional-capability-profile gap. Process-and-outcome integrated metrics (AgentAtlas direction) addresses the outcome-only-leaderboard gap. Reward-hacking-resistant scoring methodology (UC Berkeley CDRI direction) addresses the benchmark-gaming attack surface. The H2 2026 to 2027 evaluation-infrastructure research direction needs to integrate all three.

The procurement-evaluation implication

Enterprise procurement teams currently evaluating agents against the established six benchmarks should plan to transition to holistic-cross-benchmark evaluation as the infrastructure matures. The transition timeline is probably 12-18 months — long enough that current procurement should use the established benchmarks while preparing for the methodology shift. Vendors with deployment-simulation tooling, internal replay-evaluation infrastructure, and process-quality metrics are best-positioned for the transition.

What this means for the agent-benchmark category broadly

The H2 2026 to 2027 agent-benchmark category needs to evolve from the H1 2026 consolidation state. Either established benchmarks add holistic-evaluation infrastructure and reward-hacking-resistant scoring (gradual evolution), or new benchmark suites emerge that integrate these requirements from the start (revolutionary change), or the field bifurcates between research benchmarks and procurement-evaluation benchmarks (parallel evolution). All three trajectories are coherent through the H2 2026 to 2028 window.

arXiv — Holistic Agent Leaderboard: The Missing Infrastructure for AI Agent Evaluation → · Decode The Future — AI Agent Benchmarks 2026: 6 Tests That Matter →