However, there is a potential catch with these powerful capabilities for autonomous action throughout systems and applications. What if the agentic AI agent makes a mistake along the way? Errors can cascade if not controlled and corrected.
It’s virtually impossible to ignore how rapidly artificial intelligence has evolved over the past few years.
As we’ve covered in past articles, AI has transformed IT support, unified communications, contact centers, and remote work, among many other aspects of modern business and life.
AI has begun a new phase of evolution—one that offers far greater agency and automation but requires a higher level of attention, planning, and strategic oversight than before. That phase is agentic AI, and it’s become the hottest topic of enterprise technology today.
In 2026, 25% of organizations that currently use generative AI are piloting agentic alternatives—that figure is projected to double by 2027. However, it’s also predicted that more than 40% of those organizations will cancel their agentic AI projects by the end of 2027.
Why? Cost escalations, a lack of clarity in ROI, and general risk avoidance.
The bottom line is that the hype behind agentic AI is real, but its advanced capabilities are a double-edged sword that requires thoughtful consideration for successful deployment.
In this article, we’ll show you what agentic AI actually is, how it’s different, and the unique challenges you’ll need to overcome in order to harness it for your organization.
Agentic AI is a significant leap forward in terms of automation and autonomous functions. Platforms like ChatGPT and Microsoft Copilot use generative AI. While this version of AI can generate a near infinite variety of content and outputs, it is reactive in nature. It requires prompts from the user and corresponding training data to complete tasks.
Agentic AI can autonomously pursue complex, multi-step goals with limited human interaction and direction. In practice, agentic AI systems can not only create content and outputs, it can also access APIs, query databases, trigger workflows, update records, and coordinate with other AI technologies—all without a human directing and approving individual steps.
As we touched on in our article on AI tools for IT support, agentic AI platforms like Moveworks already are able to autonomously resolve support tickets through API calls to backend systems. As agentic AI continues to evolve, these capabilities will broaden, offering a variety of businesses massive potential gains in productivity and cost savings.
However, there is a potential catch with these powerful capabilities for autonomous action throughout systems and applications. What if the agentic AI agent makes a mistake along the way? Errors can cascade if not controlled and corrected.
It’s one thing to learn about the general nature of agentic AI; it’s another to see the actual capabilities of current platforms and envision where they could live in your technology stack.
RingCentral shows what this technology can do in terms of omnichannel communications and customer support. RingCentral is so committed to agentic AI that it describes itself as an “Agentic Voice AI platform” that can “automate, assist, and analyze customer interactions end-to-end.” RingCentral has integrated agentic AI throughout its portfolio, offering capabilities like:
Most notably, RingCentral recently announced the release of AIR Pro and AIR Pro Studio. It expands upon their existing AI Receptionist, enabling organizations without technical expertise to design intelligent AI agents that integrate with enterprise systems to automate a variety of customer interaction tasks, such as scheduling appointments, managing billing, and updating databases.
Although best known for video meetings and conferencing, Zoom is also a strong contender in the omnichannel communications space. Similarly to RingCentral, Zoom has integrated agentic AI throughout its portfolio, including Zoom Workplace, Zoom Phone, and Zoom CX. The agentic component offers capabilities for automated workflow orchestration for end-to-end follow up with meetings, calls, and customer interactions across channels.
As a specific example, Zoom’s AI Expert Assist 3.0. can take action directly via approved workflows across enterprise systems—updating records, triggering follow-up processes, and automatically documenting interaction outcomes—while providing full visibility into AI recommendations and actions. But the centerpiece of Zoom’s agentic capabilities is AI Companion 3.0. Built on four core agentic skills—reasoning, memory, task action, and orchestration—AI Companion 3.0 enables organizations to build custom AI agents and workflows that integrate with third-party enterprise systems including Salesforce, ServiceNow, and Google Drive.
Although Dialpad also offers agentic AI capabilities for omnichannel communications, this platform focuses on elevating the customer experience (CX). What makes Dialpad’s approach different is that agentic AI is built directly into the platform, rather than being layered on top of a separate system.
Dialpad’s AI agents can autonomously handle voice and digital customer interactions across multiple channels—resolving inquiries, scheduling appointments, processing orders, and managing escalations—while passing full conversation transcripts and context to human agents. The tradeoff is that Dialpad works best within its own customer-service-focused communications system. In contrast, RingCentral and Zoom offer broader cross-functional enterprise automation capabilities.
As a Platinum-certified Mitel partner, we have extensive experience with Mitel CX’s agentic AI capabilities. Mitel CX 2.0 introduced AI virtual agents capable of handling customer inquiries independently and intelligently routing complex cases to human agents.
Beyond virtual agents, Mitel CX 2.0 introduced agentic workflows that can autonomously execute multi-step actions—such as issuing support tickets, sending notifications, placing orders, and initiating approvals—without step-by-step human intervention. What makes Mitel CX stand out is deployment flexibility for unified communications. Unlike the cloud-native platforms we covered above, Mitel CX supports on-premises, cloud, and hybrid deployments.
While new technologies are inevitably exciting, the autonomous capabilities of agentic AI do come at risk—they can make mistakes, and if given access to a variety of systems and apps, those mistakes can cascade. Take PocketOS, an automotive software company, as a cautionary tale. In April of 2026, an agentic AI coding agent deleted PocketOS’ production database and backups in nine seconds.
So before committing to agentic AI, make sure that a traditional automation or generative AI solution wouldn’t suffice. A few questions to ask are:
Agentic AI systems can connect to APIs, access databases, interface with ticketing systems, and can even execute scripts, modify configurations, and send communications. In terms of security, agentic AI agents pose the same threat as any human employee.
So just like your human employees, agentic AI should be subject to the principles of Zero Trust Architecture and minimum necessary access. That means they should operate as a first-class privileged identity with tightly scoped permissions, lifecycle management, and access controls.
Agentic AI can execute numerous actions across integrated systems within a matter of moments. That makes it critical to ensure that every AI agent action—from authentication events to decision handoffs and everywhere in between—is tracked by a log that can be monitored in real time.
Ownership of agentic AI is also critical to establish from day one—not after an incident occurs. Designate a team member or team to lead agentic operations, and with the real-time log, task them with monitoring AI agent behavior, performance, and risk.
The core value of agentic AI is its ability to autonomously interact with wide-ranging systems, apps, and processes. This can be very complex from a technical perspective as it’s not enough to confirm that an agent or platform can connect to a system. You also need to ensure that the integration is stable at scale, test what happens when systems are interrupted, and confirm that data flow is governed and fully secure.
Start small with a single, clear use case. This will enable your team to fully test the technical infrastructure, governance frameworks, and measurements for success and failure. With this experience, you can validate the ROI and scale with confidence moving forward.
Agentic AI offers the potential for game-changing productivity gains, but the autonomous, connected nature of the technology significantly raises the stakes for deployment. One way to ensure success is to enlist a partner with decades of experience supporting complex communications and IT infrastructure for businesses of all sizes.
Not sure where to start? Explore our Technology Evaluation and Consulting solutions. Whether you’re evaluating specific platforms or working through the integration, security, and governance challenges that come with agentic deployment—our team has the knowledge to guide you from pilot to full rollout with confidence.