The state of agent-operability in media tech
ARI measures whether autonomous software can actually operate media-and-entertainment platforms — not whether vendors talk about AI, but whether agents can discover capabilities, authenticate, orchestrate work, and economically transact through them.
Why this exists
Autonomous-agent workflows are arriving in media operations faster than vendor docs admit. A platform that can't be discovered, authenticated, or driven by software at scale becomes a bottleneck — not because its features are weak, but because its operability is. ARI scores the second.
The goal is a public, methodology-transparent benchmark that gives buyers, integrators, and the vendors themselves a shared yardstick: where each platform sits today on a 0–100 scale of agent-operability, which pillars carry the most weight, and which concrete gaps would move the score most.
How a score is built
Every product is scored against the active rubric (currently v3.1, 7 pillars, 100 points) by three independent frontier models with web grounding. The models surface signals + penalties per pillar; a deterministic scoring engine then computes pillar subtotals and a composite Total Score. Multiple-evaluator agreement raises confidence; dissent shows up as recommendations. The whole pipeline is reproducible and operator-audited.
See Methodology for the full rubric, scoring math, and grading bands, or Rankings for the current leaderboard.
What this isn't
- Not a quality or feature ranking. A platform can have outstanding domain features and still score poorly on agent-operability.
- Not a paid placement or a vendor scorecard. Vendors don't see scores before publication and don't pay to be included.
- Not a one-shot benchmark. Releases are versioned; products are re-scored as the rubric and as vendor surfaces evolve.