How to turn shadow AI into a safe agentic workforce: Lessons from Barndoor AI

At most enterprises right now, Shadow AI is the new BYODIf this feels familiar, it’s because we’ve seen this movie before.The panel drew a direct line back to the early mobile and cloud eras. BlackBerry and early smartphones were not adopted because IT blessed them in a strategy document. They were adopted because sales teams bought them with their own budgets, used them to close deals faster, and forced the organization to catch up.AWS snuck in through side doors when servers got cheap enough for engineers to expense them. One large company famously miscounted its server fleet by 100,000 machines because so much of the infrastructure had been acquired locally, not centrally.The same pattern is happening with AI and MCP:It’s trivial for a developer or power user to download an open-source MCP server and connect it to Claude or Cursor.Many of those Agentic AI as “enthusiastic interns”One of the more memorable metaphors from the session was this:Think of your AI agents as very enthusiastic interns.They are eager. They are fast. In many cases, they are surprisingly capable. But they lack context. They don’t understand your culture, your history with a customer, or the subtleties of your regulatory environment. If you give them access to everything on day one, you are setting them – and yourself – up to fail.With human interns, we intuitively understand this. You bring someone in. You:Give them limited access to systems.Ask them to complete specific tasks.Watch how they go about it.Increase their scope as they demonstrate judgment and reliability.If they handle sensitive information poorly or break the process, you pull them back, coach them, and reassess.Agentic AI needs the same pattern – but encoded into the infrastructure, not left to informal norms.This is the space Barndoor wants to create solutions in: governing what AI agents can see and do across MCP, systems of record, and enterprise workflows, with the same seriousness we apply to human identity and access management.From hidden wins to repeatable successOne of the most useful points in the panel was subtle but important: governance isn’t just about catching bad things. It’s about discovering good things.If you have no visibility into MCP traffic, you don’t just miss security issues. You also miss:The engineer who quietly automated a tedious reconciliation workflow.The support team that wired an agent to resolve certain ticket types end-to-end.The operations manager who built an AI-driven scheduling workflow that saved hours each week.In a world without a control plane, these wins stay local. They live in private repos, personal workflows, and small teams. They never turn into organisational patterns.With a proper governance and observability layer, you can:See which AI workflows are emerging.Quantify their impact.Turn them into reusable patterns for other teams.Learn from failures just as deliberately as you learn from successes.This is where Barndoor’s focus on “visibility, accountability, and governance” becomes non-negotiable. It’s not trying to orchestrate every agent. I-guide what’s already happening, so enterprises can move from isolated experiments to a genuine getting real value out of agentic AI.What this means if you want to be the “AI hero” in your companyThe panel ended with a simple challenge to the audience: if you want to be a hero inside your organisation, you have to play both sides.You have to acknowledge that your colleagues are already using AI, sometimes in ways that make your security team nervous. And you have to help design a path where:Experimentation is encouraged, not punished.Failure is treated as learning, not as a reason to shut things down.Governance is baked into the plumbing, not bolted on at the end.AI agents are treated like interns: limited at first, then progressively trusted as they prove themselves.That’s not a role that belongs solely to vendors or solely to internal teams. It’s a partnership.Barndoor’s bet is that enterprises will need a dedicated control plane for this – something that is built for AI agents, MCP connections, and complex policies deeply enough to be more than “identity, but with a new coat of paint.”Whether or not you adopt Barndoor specifically, the underlying idea is hard to ignore:If we want AI agents to stop living in the shadows and start doing real work at scale, we need to give them the same kind of structured, observable environment we give human workers, but built for AI. Granular permissions. Training wheels. Feedback loops. Visibility.The companies that break that win will be the ones that treat governance not as a gate, but as the infrastructure that makes agentic AI genuinely safe, accountable, and transformative.