# How to Govern Autonomous AI Agents with Secure API Controls

Jason, who works in AI enablement and operations at Ally Bank, explains API governance for autonomous agents by comparing them to bears that follow incentives and push through any opening they are given. He describes common failure modes such as looping agents, tool-checking “door” agents, hijacked context, hidden zombie runs, and “mullet bear” cases where everything appears normal until the backend is a mess. A central example shows how a support agent incorrectly issued a $1,200 refund instead of the intended $200 correction, then kept retrying because the case never closed, creating 49 runs, 448 refund attempts, and major cost exposure. His core lesson is that identity and privileges are fundamental: agent actions must be tied to the requesting user, limited with short-lived least-privilege credentials, checked for evidence and duplicates, bounded by budgets and exit criteria, and monitored by both the agent runtime and API platform. He also argues for end-to-end system evaluations to catch these edge cases before production.