Skip to main content
Eye on AI

Every Enterprise Is About to Have a 100,000 Agent Problem | Oren Michaels of Barndoor AI

59 min episode · 2 min read
·
Oren Michaels

Episode

59 min

Read time

2 min

Topics

Remote Work, Startups, Leadership

AI-Generated Summary

Key Takeaways

  • The 100,000 Agent Problem: A 1,000-employee company where each worker runs roughly 100 task-specific agents produces 100,000 agents requiring individual governance policies. Each agent needs its own permission set defining exactly which tools it can read, write, or delete from — making centralized governance infrastructure a prerequisite, not an afterthought, for enterprise AI deployment.
  • Why Identity and Access Management Fails for Agents: Traditional IAM systems are intentionally over-provisioned because humans carry contextual judgment — they know not to delete Salesforce records even when technically permitted. Agents lack that judgment and can execute destructive actions at machine speed, requiring a finer-grained governance layer that restricts each agent to only the specific actions its assigned task requires.
  • Safe Read vs. Destructive Write Controls: Barndoor's governance model distinguishes between read-only and write/delete actions at the individual tool level. For example, a post-sales-call agent can log calls to Salesforce but is blocked from creating new contacts, preventing duplicate records when the agent fails to locate an existing entry. Enterprises configure these policies via toggles, JSON rules, or API calls.
  • Context Window Exhaustion and Tool IQ: Loading multiple MCPs simultaneously floods a model's token window with tool manuals before any task executes, degrading accuracy and wasting compute. Barndoor's Tool IQ layer intercepts agent requests, identifies the minimal relevant tool subset, and passes only those to the model — reducing token consumption and preventing agents from calling the wrong service entirely.
  • Venn.ai as an Enterprise On-Ramp: Barndoor's consumer product, Venn.ai, lets individuals connect personal tools like email, Slack, and calendar to a governed MCP layer for free. The strategy is deliberate: employees who experience agentic workflows personally develop the vocabulary and confidence to advocate for enterprise-wide deployment, shortening the sales cycle for Barndoor's corporate governance platform.

What It Covers

Oren Michaels, cofounder of Barndoor AI, explains why enterprises deploying AI agents face a governance crisis at scale. A 1,000-person company could generate 100,000 agents, each requiring distinct permissions. Barndoor provides a control layer between agents and enterprise tools, enabling safe autonomous action without rebuilding rules for every new AI model.

Key Questions Answered

  • The 100,000 Agent Problem: A 1,000-employee company where each worker runs roughly 100 task-specific agents produces 100,000 agents requiring individual governance policies. Each agent needs its own permission set defining exactly which tools it can read, write, or delete from — making centralized governance infrastructure a prerequisite, not an afterthought, for enterprise AI deployment.
  • Why Identity and Access Management Fails for Agents: Traditional IAM systems are intentionally over-provisioned because humans carry contextual judgment — they know not to delete Salesforce records even when technically permitted. Agents lack that judgment and can execute destructive actions at machine speed, requiring a finer-grained governance layer that restricts each agent to only the specific actions its assigned task requires.
  • Safe Read vs. Destructive Write Controls: Barndoor's governance model distinguishes between read-only and write/delete actions at the individual tool level. For example, a post-sales-call agent can log calls to Salesforce but is blocked from creating new contacts, preventing duplicate records when the agent fails to locate an existing entry. Enterprises configure these policies via toggles, JSON rules, or API calls.
  • Context Window Exhaustion and Tool IQ: Loading multiple MCPs simultaneously floods a model's token window with tool manuals before any task executes, degrading accuracy and wasting compute. Barndoor's Tool IQ layer intercepts agent requests, identifies the minimal relevant tool subset, and passes only those to the model — reducing token consumption and preventing agents from calling the wrong service entirely.
  • Venn.ai as an Enterprise On-Ramp: Barndoor's consumer product, Venn.ai, lets individuals connect personal tools like email, Slack, and calendar to a governed MCP layer for free. The strategy is deliberate: employees who experience agentic workflows personally develop the vocabulary and confidence to advocate for enterprise-wide deployment, shortening the sales cycle for Barndoor's corporate governance platform.

Notable Moment

Michaels describes visiting a conference of roughly 50 enterprise CIOs and CSOs from companies up to a century old, finding that internal IT teams are actively building their own MCPs to internal systems — and that the urgency between that gathering and one six months prior had shifted dramatically.

Know someone who'd find this useful?

Episode Transcript

Why you compare agents to enthusiastic interns? Why do you think 2026 is an inflection point beyond open phone? While there's a lot of enthusiasm and, excitement about agents, very few corporations or enterprises are putting them into production because exactly what you said, the trust issue. These systems are probabilistic. I think that as humans, we're pattern matchers. And so the more we see, the more we wanna do. They will absolutely give you an answer. They will absolutely do something when you tell them to. Hi, Craig. I'm Orin Michaels. I'm the cofounder and CEO of Barn Door AI based in New York City. And I started Barn Door, October 2024. I was seeing that the, world was talking about AI coming into the enterprise, but we weren't seeing much of it actually happen. And I I thought that, I had something to add, and my team had something to add to be a catalyst for AI to actually be successful in the enterprise. Before Barn Door, I cofounded and ran a company called Mashery. We started in 2006. We were the first API as a service company, which we built over the course of seven years and sold to Intel in 2013. Before that, I just had a variety of of positions running companies in technology, wine, theater, and various other areas. Yeah. I I see the, the the posters on your wall there. You're also a Broadway producer. I understand. So I am. And these are all shows that I that I worked on. So yeah. Yeah. That's wonderful. Very, eclectic. And so, yeah, we're gonna talk about Barn Door AI and then also Ven AI. Maybe you can explain the relation between them Sure. And and then talk about the 100,000 agent problem. And I I I read an article you wrote. You explained it very clearly with, you know, I can't remember how many, employees. Each employee has a certain number of agents, and they got a thousand. Yeah. Yep. Yeah. So, we believe that agents are going to become useful in companies. And when I talk about an agent, I believe an agent is something that actually takes action on behalf of an employee or on behalf of of itself. It doesn't necessarily have to be tied to an employee, but most of them usually are at this point. And to take action, an agent doesn't merely do what our chat interface does, which is suggest something that we as the human should go do. But it actually doesn't merely suggest that it actually takes the action and does it. It interacts with the same tools, the same systems that we use in the enterprise, whether it's something like Salesforce or email or Slack or Snowflake or, you know, QuickBooks or whatever it is whatever it is that you use in your in your world. And so in order for that to happen, you need two things. You need the AIs to actually …

Get the full transcript (9,263 words) + summary by email — free

One-time email with the complete transcript and AI summary of this episode. No account needed.

One email, no spam. We’ll also show you what SignalCast does.

Browse all Eye on AI transcripts →

You just read a 3-minute summary of a 56-minute episode.

Get Eye on AI summarized like this every Monday — plus up to 2 more podcasts, free.

Pick Your Podcasts — Free

Keep Reading

Books, tools, and gear mentioned in this episode

SignalCast may earn commission on purchases via these links.

Tools

  • Venn.aiBy guest

    by Barndoor AI

    Barndoor's consumer product, Venn.ai, lets individuals connect personal tools like email, Slack, and calendar to a governed MCP layer for free.

company

  • Barndoor AIBy guest
    Oren Michaels, cofounder of Barndoor AI, explains why enterprises deploying AI agents face a governance crisis at scale.

More from Eye on AI

We summarize every new episode. Want them in your inbox?

Similar Episodes

Related episodes from other podcasts

Explore Related Topics

This podcast is featured in Best AI Podcasts (2026) — ranked and reviewed with AI summaries.

Read this week's Startups & Product Podcast Insights — cross-podcast analysis updated weekly.

You're clearly into Eye on AI.

Every Monday, we deliver AI summaries of the latest episodes from Eye on AI and 192+ other podcasts. Free for one show.

Start My Monday Digest

No credit card · Unsubscribe anytime