Enterprises don't deploy single agents in isolation—they deploy fleets where each agent calls APIs, other agents, and reaches into applications never designed for machine decision-makers. The actual failure mode is a tangled, opaque system nobody can see clearly enough to govern. Complexity doesn't grow linearly with agent count; it compounds exponentially with the number of paths between agents. A support ticket that once touched one system may now pass through four agents before human review, with each handoff representing an unauthorized decision point.
Most enterprise AI programs stall when responsible humans lose visibility into their agents. Security teams often cannot answer which agents can reach which systems, or which agent triggered a downstream action three hops prior. The common instinct is a checklist approach—approve the agent, log it, move on—but this fails because complexity runs across chains. One-time approvals cannot govern interconnected, cascading systems any more than eating one vegetable constitutes a successful diet.
Permissions creep compounds the problem. Broad API access granted to an agent for convenience can create unintended paths into sensitive systems like payments six months later. Ownership also thins out along agent chains: when five agents touch one workflow and something breaks at step four, nobody is assigned to own that link because the org chart stopped at "deploy the agent" and never defined who answers for it. The fix requires every agent to exist as its own entity with its own name, scoped authority, and named human sponsor—though this is necessary but not sufficient to address the deeper governance gap.