AI Agents Can Work Faster Than Humans—And Fail Harder Too

The increasing use of AI agents in various industries has brought about a mix of efficiency and risk. While these agents can automate tasks, manage infrastructure, and even approve transactions, they also rely on human-oriented permission models that can’t safely govern autonomous behavior. This has led to a pressing need for smarter boundaries to determine how safely enterprises can scale AI.

Understanding the Risks of AI Agents

Traditional access control frameworks were designed with human rhythms in mind, where users log in, complete tasks, and log off. However, AI agents operate on a different timescale, acting continuously across multiple systems without fatigue. This has made authorization a critical issue, with many companies trying to teach new systems to act autonomously while still managing permissions through static roles, hard-coded logic, and spreadsheets.

Graham Neray, co-founder and CEO of Oso Security, emphasizes that authorization is the most important unsolved problem in software. He notes that every company that builds software ends up reinventing authorization from scratch, and most do it badly. Now, with AI being layered on top of that foundation, the problem has become even more pronounced.

The Importance of Smarter Boundaries

The problem with AI agents isn’t intent, but infrastructure. Most companies are trying to manage permissions through static roles, hard-coded logic, and spreadsheets, which is a model that barely worked for humans. For machines, it’s a liability. An AI agent can execute thousands of actions per second, and if one of those actions is misconfigured or maliciously prompted, it can cascade through a production environment long before anyone intervenes.

Todd Thiemann, principal analyst at Omdia, explains that enterprise IT teams are under pressure to demonstrate a tangible ROI of their generative AI investments. As a result, security and identity security can fall by the wayside in the rush to get AI agents into production. However, this can lead to misuse or unintended escalation if the agent is given broad, human-equivalent permission.

Building a Safer AI Framework

To address this issue, companies need to build smarter boundaries around their AI agents. This can be achieved by granting only the permissions necessary for a specific task, for a defined period of time, and automatically revoking them afterward. This approach, known as automated least privilege, can help minimize the potential blast radius of any mistake or incident.

Oso Security is one company working to operationalize this transition, turning authorization into a modular, API-driven layer rather than bespoke code scattered across microservices. This approach can help companies strike a balance between speed and safety, allowing agents to act autonomously within clearly defined boundaries.

Trust, but Verify

Ultimately, the key to safe autonomy is to redefine where the human loop sits. Machines can handle repetitive, low-risk actions at speed, but humans should remain the final checkpoint for high-impact ones. By designing smarter boundaries and minimizing privileges, companies can minimize the potential risks associated with AI agents and ensure a safer, more sustainable deployment of these technologies.

As the use of AI agents continues to grow, it’s essential for companies to prioritize the design of their boundaries. By doing so, they can unlock the full potential of these technologies while minimizing the risks associated with them. The future of safe autonomy depends less on how smart the models become and more on how intelligently we design their boundaries.