AI systems are beginning to evolve beyond simple responses. Many organizations are currently testing AI agents that plan tasks, make decisions, and execute actions with limited human input. It’s no longer just a matter of whether the model gives the right answer. It’s about what happens if that model is allowed to act.
Autonomous systems require clear boundaries. They need rules that define what they have access to, what they allow, and how their behavior is tracked. Without these controls, even a well-trained system can experience problems that are difficult to detect or recover from.
One company working on this issue is Deloitte. The company has developed governance frameworks and advisory approaches to help organizations manage AI systems.
From tools to AI agents
Most AI systems in use today still rely on human prompts. They generate text, analyze data, and make predictions, but it’s usually people who decide what happens next. Agent AI changes that pattern. These systems can divide goals into steps, select actions, and interact with other systems to complete tasks.
With increased independence comes new challenges. When systems operate on their own, they can take completely unexpected paths or use data in ways that were not intended.
Deloitte’s efforts are focused on helping organizations prepare for these risks. Rather than treating AI as a standalone tool, the company focuses on how it fits into business processes, such as how decisions are made and how data flows through the system.
Build governance into the lifecycle
Don’t add governance after deployment. It must be integrated into the entire lifecycle of an AI system.
This starts at the design stage. Organizations must define what is allowed in the system and where its limits lie. This may include setting rules around data usage and outlining how the system should respond in uncertain situations.
The next stage is deployment. Currently, governance is focused on access and control, including who can use the system and what they can connect to. Once the system is up and running, monitoring becomes a major concern. Autonomous systems can change over time as they interact with new data. If you don’t check it regularly, it can stray from its intended purpose.
The role of transparency and accountability
As AI systems become more responsible, it becomes more difficult to track how decisions are made. This calls for greater transparency. Deloitte’s work highlights the importance of tracking how systems are performing. This includes recording actions and documenting decisions. These records help organizations determine what happened if something goes wrong. When an autonomous system performs an action, it must be clear who is responsible.
According to Deloitte research, the adoption of AI agents is growing faster than the controls needed to manage them. Approximately 23% of businesses are already using them, and that number is expected to reach 74% within two years. Only 21% reported having strong safeguards in place to monitor their behavior.
Real-time monitoring of AI agents
Once an autonomous system is activated, the focus shifts to how it behaves in real-world situations. Static rules are not always sufficient; the system must be monitored during operation.
Deloitte’s approach includes real-time monitoring, allowing organizations to track what their AI systems are doing as they perform tasks. If the system behaves unexpectedly, the team can quickly intervene. This may include pausing certain actions or adjusting permissions. Real-time monitoring also helps with compliance. In regulated industries, companies must demonstrate that their systems follow rules and standards.
In fact, these controls are starting to appear in production settings. Deloitte describes a scenario where an AI system monitors equipment performance across a site. Sensor data can signal early signs of failure, which can trigger maintenance workflows and update internal systems. A governance framework defines what actions the system can take, if human approval is required, and how decisions are recorded. Although this process runs across multiple systems, it appears as one action from the user’s perspective.
Governance will be part of the discussion at AI & Big Data Expo North America 2026, May 18-19 in Santa Clara, California. Deloitte is a Diamond Sponsor of the event, and is among the companies contributing to the conversation about how autonomous systems are deployed and controlled in practice.
The challenge is not just to build smarter systems, but to ensure that they operate in ways that organizations can understand, manage, and trust over time.
(Photo provided by Roman)
See also: Autonomous AI systems rely on data governance
Want to learn more about AI and big data from industry leaders? Check out the AI & Big Data Expos in Amsterdam, California, and London. This comprehensive event is part of TechEx and co-located with other major technology events such as Cyber Security & Cloud Expo. Click here for more information.
AI News is brought to you by TechForge Media. Learn about other upcoming enterprise technology events and webinars.
