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News
Inside Monte Carlo's GTM Hub: How We Run Our Revenue Team On AI Agents
5+ hour, 31+ min ago (1192+ words) With the proliferation of AI across businesses for internal use cases, teams are quickly spinning up agents to take on many of the tasks that humans used to do. With that inevitably comes some operational chaos. The work lives and…...
Building Autonomous Agent Trust: RL, An Agent Fleet, And Comprehensive Observability
6+ day, 3+ hour ago (1345+ words) For many organizations, getting agents to successfully run in production, particularly to the extent that they can be trusted to be completely autonomous, seems like an insurmountable challenge. Agents look great in pilots, but once they are deployed live, issues…...
Agent Trust
1+ week, 4+ hour ago (132+ words) montecarlo.ai What Is Agent Trust? Definitions, Framework & FAQ The Agent Trust gap: how enterprises need to evolve to deploy AI safely at scale Redefining AI Agent Trust for Production: An Input/Output-First Approach Read more related stories Monte Carlo’s…...
The 2026 Guide To Agent Observability Tools
2+ week, 3+ day ago (1801+ words) Agent observability tools watch your AI agents in production and tell you what they’re really doing: the data they pull in, how fast and how expensively they run, the paths they take through a task, and whether their answers are…...
How The New York Jets Are Advancing The Ball For An AI Forward Future With Monte Carlo
2+ week, 6+ day ago (553+ words) When Fusillo joined the Jets roughly eight weeks before the 2026 NFL draft, meaning the data foundation and the AI program had to be built in parallel rather than in sequence — with no existing playbook to draw from. The stakes were…...
What Is Agent Trust? Definition & Framework
3+ week, 1+ day ago (499+ words) Agent trust is the confidence that your AI agents behave correctly, reliably, and consistently in production. And your ability to demonstrate it. None of them holds up in isolation, and none can be checked periodically. Agent trust means watching all…...
Saying Bye-bye To BI
1+ mon, 5+ day ago (675+ words) Michael Segner is Head of Product Marketing at Monte Carlo. He co-authored the O'Reilly report Ensuring Data + AI Reliability Through Observability (ISBN 979-8-341-64942-2) with Barr Moses, Monte Carlo co-founder and creator of the data observability category. Michael regularly presents on data…...
How To Build An AI Native Engineering Org: What We Actually Did
1+ mon, 1+ week ago (1209+ words) In March we restructured Monte Carlo’s engineering organization. As I’ve thought about sharing our decision-making process, I’ve wanted to be far enough past the restructure to say something honest, objective, and which other engineering leaders can hopefully find useful. The…...
The 5 AI Agent Evaluation Metrics Every Team Should Be Tracking
2+ mon, 1+ day ago (756+ words) So you built an AI agent. Great. You shipped it, your boss is impressed, and somewhere a CFO is staring at an Anthropic invoice with the kind of quiet horror usually reserved for medical bills. Welcome to the part of…...
Built For The Enterprise, Ready For AI: Under The Hood Of Monte Carlo's Platform Updates
2+ mon, 4+ day ago (971+ words) We’ve been building out the platform layer for years — deployment, networking, identity, governance, compliance. Two newer pieces stand out: Here’s a quick tour. We named our collection agent before “AI agent” became the entire industry’s obsession. It’s the customer-side component…...