- The autonomous nature of agentic AI introduces new security and operational risks.
- Agents thrive on connectivity to enterprise systems, but making it happen securely requires strong identity, permission, and auditing controls.
- Agentic AI initiatives should prioritize ongoing monitoring and oversight rather than one-time governance policies.
It’s easy to think of agentic AI as just another product feature.
But it’s much more than that.
In the agentic world, software is fundamentally different. System design, governance, and organizational decision-making are radically altered. Understanding the tangible impact of agentic AI is essential as you’re evaluating, deploying, and managing it.
In short, here’s the shift: software used to depend on people to tell it what to do. Now, AI agents can make decisions and execute them on their own. About 74% of companies are expected to be using agentic AI within the next two years.1 But the critical, unanswered question is how enterprises will manage, secure, monitor, and scale it.
Why Agentic AI Requires a New Approach
Agentic AI is powerful, and the potential for unintended consequences and negative outcomes is undeniable. Three architectural elements contribute to this reality.
The ReAct Loop
Agents function differently than chatbots: they continuously reason and act. They look at the situation, make a decision about their next action, execute, then examine the results. This process repeats over and over again. Chatbots, on the other hand, simply respond to a single prompt. Each phase of the agentic loop builds on the last, which can amplify risk—especially at scale.
Tool Use and MCP
When AI agents connect to critical business systems, amazing things can happen. This capability is primarily enabled by the Model Context Protocol (MCP), which allows agents to conduct online searches, access documents, update records, send emails, and work across multiple applications. The downside is that this kind of rich connectivity also represents a new attack surface. Agents with access and execution capabilities on business systems should be governed the same way as any other account with elevated permissions.
Multi-Agent Architectures
Advanced agentic AI deployments involve multiple agents working in concert. Typically, these involve an overseeing agent that distills a larger goal into smaller tasks and delegates them to specialized sub-agents. Tasks happen simultaneously, which increases speed and efficiency, but also introduces coordination requirements.
Agentic AI Is More Than Automation
It’s easy to think of agentic AI as the next evolution in automation. But that perspective, while mostly correct, overlooks a critical difference. Traditional automation is based around predictable inputs and outputs. That’s why it works so well.
AI agents introduce unpredictable variability. They can take different paths to get to the same goal. This flexibility is part of what makes them so powerful, but it also means you don’t always know exactly how they’re going to tackle a given task.
Since agentic behavior is always evolving, “set and forget” is not an option. Organizations require defined checkpoints for human intervention and ways to identify anomalies and problematic behavior.
Asking the Right Questions
With a vendor leading the conversation, many teams find themselves first asking, “Which platform should we choose?” But the more important question is whether your organization is ready for technology that acts on its own.
Considerations such as permissions, security, and audit trails are just as critical at the new agentic capabilities. Modern systems are designed around people, not autonomous agents. Closing that gap is critical to capturing the potential of agentic AI effectively and securely.
A: Agentic AI is considerably more dynamic and unpredictable. Agents can analyze tasks, select a course of action, use tools, evaluate results, and change their approach on the fly—all without human intervention.
A: Agents thrive on access to business systems, data, applications, and workflows. This access essentially allows them to act like privileged users. Organizations need strong permissions, monitoring, and audit trails at scale to prevent mistakes and thwart attacks.
A: Don’t start by asking, “Which platform should we use?” Instead, organizations need to assess whether they’re ready for software that can reason and act on its own. This should involve evaluating governance, security, identity, monitoring, escalation paths, and human oversight.
- Deloitte, State of AI in the Enterprise: The untapped edge, January 2026
