Hi, it's Ravit 👋
Most conversations about "AI in production" happen in environments with soft landings. A hallucination gets caught in review. A bad pipeline gets patched before Monday.
The New York Jets don't have that luxury.
They're running AI agents inside one of the most unforgiving operating environments in professional sports — real-time, high-stakes, and completely exposed. There is no hiding from a bad output when your organization performs in front of millions.
And here's the part that made me stop: they scaled to 10x agent growth while executing a full data migration at the same time.
On July 23, Barr Moses (Co-Founder & CEO, Monte Carlo) and Iwao Fusillo (Chief Data & Analytics Officer, New York Jets) are breaking down exactly how — in a candid, executive-level conversation.
🗓️ July 23, 2026 | ⏰ 10:00 – 11:00 AM PT | 💻 Virtual
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Nobody's talking about the oversight gap enough
Here's the uncomfortable math every data + AI leader eventually runs into:
Agent adoption grows exponentially. Human oversight capacity doesn't grow at all.
At 2–3 agents, a data team can manually sanity-check outputs. At 10x that number, with agents making decisions on top of live data — some of it mid-migration — manual review isn't just inefficient. It's mathematically impossible.
That crossover point is where observability stops being a dashboard and becomes load-bearing infrastructure. It's the difference between knowing your data is clean and knowing your agents are actually behaving the way you think they are — at scale, in real time, with real consequences.
The Jets hit that inflection point. This webinar is about what they built in response.
What's on the whiteboard
1️⃣ The 10x inflection point every CDAIO needs to prepare for — why there's a specific scale threshold where agent oversight breaks down, and how to recognize you're approaching it before something ships that shouldn't.
2️⃣ Agent trust in a zero-margin environment — how the Jets approach reliability and accountability when AI is in the decision loop: what gets monitored, what gets gated, and who's accountable when the margin for error is zero.
3️⃣ Running a migration and an agent scale-up simultaneously — conventional wisdom says freeze your AI roadmap during a migration. The Jets ran both concurrently. That's only survivable if your observability layer catches schema breaks, drift, and silent failures before an agent acts on broken data.
4️⃣ Autonomous observability — how Monte Carlo's AI-powered system flips monitoring from reactive (alert → triage → root-cause) to proactive, so teams scale trust at the same rate they scale agents — without scaling headcount.
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A category creator meets a zero-margin operator
Barr Moses pioneered the data + AI observability category and built Monte Carlo into the platform the world's most data-driven organizations rely on to cut data + AI downtime. Named a Top 25 Data Management & Analytics Executive of 2025 and a VentureBeat Top Woman in AI. I've known Barr for years — she doesn't do surface-level conversations.
Iwao Fusillo's résumé reads like a masterclass in enterprise data leadership: American Express, the NFL, General Motors, PepsiCo, EXL. Now he's applying all of it in an environment where every data decision plays out in public.
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Who should be in the (virtual) room
CDAIOs & data executives mapping their own agent-scale inflection point
Data platform & engineering leads who own reliability when agents consume their pipelines
AI/ML engineers shipping agents on enterprise data and wondering what "production-grade trust" actually requires
Anyone mid-migration who's been told to pause AI work until it's done (the Jets would disagree)
The teams that figure out agent observability now — before the 10x wave hits them — are the ones that get to scale AI with confidence instead of firefighting it.
An hour on July 23 could save your team a very painful quarter.
Can't make it live? Register anyway — that's how you get the recording.
See you there,
Ravit Founder & Host,
The Ravit Show
P.S. — I'll be watching the Q&A closely. Reply to this email with your hardest question about agent reliability or autonomous observability, and I'll do my best to get it asked live.
