Businesses can use AI agents without losing control by keeping agents scoped to specific workflows, giving them only the access they need, requiring review for high-risk actions, and running them through applications with clear data and permission models.
The governance question is becoming urgent because adoption is moving faster than operating discipline. McKinsey found that 62% of organizations were at least experimenting with AI agents in 2025, while nearly two-thirds had not yet begun scaling AI across the enterprise. That is a classic risk pattern: many experiments, uneven maturity, and pressure to show progress.
Why do companies worry about losing control?
AI agents can act, not just answer. That creates new operational questions. What can the agent see? What can it change? Who approved the action? How is the action logged? What happens when the agent is wrong?
What controls should be in place?
Scope limits: define exactly which workflow the agent supports.
Permission boundaries: the agent should inherit user access instead of bypassing it.
Human approval: require review before risky or irreversible actions.
Audit trails: record what the agent did, when it acted, and on whose behalf.
Change management: test and review changes before exposing new agent capabilities.
What is agent sprawl?
Agent sprawl happens when teams create many small AI workflows without shared governance. Each agent may seem useful on its own, but the portfolio becomes hard to audit, secure, support, and improve.
This is why governance vendors are now talking about agent registries, trust scores and continuous oversight. Collibra’s AI Command Center launch explicitly describes the move from passive AI oversight to active lifecycle management for agents, models and use cases.
How can a platform reduce risk?
A platform can reduce risk by standardizing identity, data structure, deployment, permissions, integrations, and lifecycle management. That matters because agent governance is much easier when the underlying apps follow a consistent model.
Where does Buzzy fit?
Buzzy helps teams create structured business applications that can be governed from the start. Instead of asking an AI agent to work across scattered spreadsheets and one-off tools, teams can give it a defined app surface with clear records, workflows, permissions, and extension points.
FAQ
Should agents be allowed to make changes automatically?
Only for low-risk actions with clear rules. Higher-risk actions should require approval or review.
Is governance only an enterprise concern?
No. Any team using AI agents with real business data needs basic controls.
What is the first governance step?
Define the workflow boundary and permissions before connecting the agent to live data.