This episode was recorded live from the Ai4 conference podcast pavilion, where host Mac Goswami sat down with Barr Moses — CEO & co-founder of Monte Carlo, creator of the data observability category and now builder of the agent trust platform — about what it actually takes to make AI trustworthy in production.

What's Covered:

"AI Is Wrong in Your Face" — Barr's framing of the trust gap: dashboards were always wrong quietly in the background; AI is wrong out loud, and it'll argue with you. Why trust is the biggest thing standing between pilots and production.

The Four Layers of Agent Failure — Context, performance, behavior, and output. Why all four can look perfect and the agent still fails — and why you have to watch all of them together.

The Flight That Already Left — The airline agent that recommended a flight that departed that morning. The agent was fine; the context was stale. The most surprising failure mode nobody plans for.

Where to Start — Make ONE agent great, not a hundred. And why the hardest first step is simply defining what "good" even looks like.

The Reinforcement Loop — The idea Barr's most excited about: agents that self-identify what went wrong, propose a fix, submit a PR for human approval, and use it as tomorrow's baseline. Agents that rebuild themselves every day — running in production today.

100% AI-First — Why every line of Monte Carlo's code is AI-generated, how it made them 3–5x faster, and Barr's stoplight analogy for where human-in-the-loop is heading.

Key Quote: "Dashboards are wrong in the background. AI is wrong in your face — it'll argue with you."

Connect with Barr: LinkedIn: Barr Moses : https://www.linkedin.com/in/barrmoses/

Monte Carlo: https://www.montecarlo.ai

Follow and subscribe to AsembleAI: Spotify | Apple Podcasts | Amazon Music | iHeart Radio | YouTube | Substack

ActualyzeAI came out of stealth just days before this conversation. Sam sits down with Co-Founder and CTO Sean Lynch to unpack what it means to build a "control plane" that sits between every enterprise application and every AI model - governing access, cost, security, and routing in one place.

Topics covered:

• What a control plane for enterprise AI actually does, and why it requires zero code changes to adopt
• Aggregating inference across OpenAI, Anthropic, Google Bedrock, and self-hosted/on-premises models into a single endpoint
• "Virtual models" — purpose-built model configurations that route requests based on task type (coding, reasoning, agentic work)
• Guardrails: automatic detection and redaction of PII, PHI, API keys, and other sensitive data in the inference stream
• Financial operations as the leading driver of adoption — budgetary controls, spend limits, and team-based tracking
• The coming wave of domestic and open-weight small language models, and why that's expanding the market
• Why model-agnostic infrastructure is critical as the foundation model landscape fragments
• How ActualyzeAI's founding team (formerly of Metacloud, acquired by Cisco) shaped their approach
• ActualyzeAI's design partner program for early enterprise customers

Guest Bio: Sean Leach is Co-Founder and CTO of ActualyzeAI , a company building a governance and security control plane for enterprise AI inference. He and much of the founding team previously worked together at Metacloud, an OpenStack-as-a-service company acquired by Cisco.

Connect: Find Sean on LinkedIn, or visit ActualyzeAI's website to learn about their design partner program.

This episode was recorded live from the Ai4 conference podcast pavilion, where host Mac Goswami sat down with Oshri Moyal — CTO & co-founder of Atera, the autonomous IT platform whose agent Robin recently ranked #1 across 15 G2 Summer 2026 reports — about what genuinely autonomous IT actually looks like.

What's Covered:

AI That Fixes, Not Just Chats — Why Robin isn't another chatbot. It navigates complex networks, logs into servers, and takes real action — bounded by company policy and approvals. Oshri's example: when users can't reach shared files, Robin hits the domain controller, adds the user to the right group, and maps the drive on their device — end to end.

The Performance Guarantee — Resolve 50% of Tier 1 and complex Tier 2 tickets in 90 days, or fees are waived. Why Atera can stand behind that after two years in production.

Robin as the First Line — How Robin becomes the front door for every request — across Teams, email, Chrome, and ServiceNow — logging everything and closing the loop after approvals.

"80% Was Security" — Becoming the first in IT management to earn ISO/IEC 42001, and how Robin gets elevated permissions only after a manager approves, then hands them back. As Oshri puts it, "80% of the project was about security, privacy, and safety."

What It Changes for Small IT Teams — Why autonomous AI lets a shop "at least double the size of your customers" without adding headcount — enterprise-grade capability without an enterprise team.

Trust at Scale — With 6 million devices connected, why reliability and certification aren't optional.

Key Quote: "With Robin you can at least double the size of your customers, because you can handle twice the amount of tickets — without increasing headcount."

Connect with Oshri: LinkedIn: Oshri Moyal : https://www.linkedin.com/in/oshr1/

Atera: https://www.atera.com/

Follow and subscribe to AsembleAI: Spotify | Apple Podcasts | Amazon Music | iHeart Radio | YouTube | Substack