Summary: AI and low-code tools now let almost any employee build working automations, and a Forbes expert panel (reported by TechOrange on October 14, 2026) argues that the resulting sprawl is outpacing oversight. Their core proposal is simple: no AI agent ships without a named individual owner, and every agent runs inside a governed layer with permissions, audit trails, and a kill switch. For teams shipping internal AI tools, this shifts governance from a post-launch checklist to a design requirement.
Why AI agents are harder to govern than RPA
The comparison that frames the whole discussion comes from OneReach.ai’s analysis: this is not the first time companies handed build power to the people closest to the work. A decade ago, RPA did the same thing. But RPA bots were brittle — a UI change broke them loudly, so problems surfaced fast.
AI agents are different. They can route around obstacles and keep going. When one drifts outside its intended scope, the result isn’t an error message; it’s an outcome nobody approved. Silent success is a harder governance problem than loud failure.
The rule: no owner, no launch
Several experts on the Forbes panel converged on one requirement. Michael Jaszczyk of NEWWORK Software says no AI agent should go live without a named owner — every agent needs a defined role, a manager, and permissions limited to what that role actually requires, with every action traceable to a responsible employee.
OneReach.ai sharpens the point: the owner must be a person, not a team. Owners answer for the agent’s behavior and are responsible for decommissioning it when it’s no longer needed. Agents should log in with dedicated service accounts — not the builder’s personal credentials — under least-privilege access.
Chris Jay Hawkinson of Hawksroost frames it as a single question every citizen-built tool must answer before launch: “who is accountable for this tool’s decisions?” If you can’t produce a name, it doesn’t ship. His reasoning is practical — registries and review gates fail because nobody owns the entries; accountability itself generates the record.
Visibility has to happen at runtime
An owner alone isn’t enough. Gregg Aldana of Appian argues all agents, low-code apps, and automations must run through a governed collaboration layer with named owners, permission boundaries, audit trails, and a kill switch. His distinction is worth remembering: a registry tells you what existed yesterday; runtime governance tells you what is operating right now.
Abhinav Shashank of Innovaccer adds that read access and execution authority are different permission tiers and should be logged separately — from the moment the tool is built, not after something breaks.
The risk extends to credentials. Damon Fleury of SpyCloud warns that API keys, tokens, and service accounts created by these tools form “shadow access” — privileged connections nobody provisioned, owns, or monitors — and attackers are already exploiting that gap.
Governance as design, not checkpoint
Two experts push beyond controls entirely. OneReach.ai argues that traditional governance treats review as a post-build checkpoint, by which time the agent is live and its connections are set. Its answer is “governance by design”: access controls, logging, and data-handling rules enforced by the architecture at build time. Casey Kindiger of Droma goes further, suggesting standards like MCP to unify the tool layer, with agent autonomy granted or revoked based on measurable trust.
What this means if you’re building internal AI tools
The advice maps cleanly onto practices you may already have. Same principle as the admin controls we covered when Copilot added model picker governance for teams: capabilities need explicit configuration before users touch them, not after.
A practical starting sequence: require a named owner field on every internal tool before deployment, use service accounts instead of personal credentials, log reads and actions separately, and attach a kill switch to anything touching systems of record. None of this slows builders down much — and it’s far cheaper than discovering an unowned agent’s decisions after the fact.
The honest limitation: registries and owners are controls, and controls don’t scale with how fast employees build. The direction the experts point toward — governance enforced by the platform itself — is where most teams will eventually need to land.