What is Agentic AI?
According to Forbes, “‘Agentic AI’ refers to AI systems that can operate and evolve autonomously to solve complex challenges, adapting to unique circumstances without the need for human intervention.” Essentially, Agentic AI systems are aiming for full automation. All the human needs to do is hit start and receive an output.
Rather than ‘human-in-the-loop’ systems, where the human operator functions as the central node in an ecosystem of AI tools, Agentic AI promises ‘human-in-the-lead’ solutions. The AI takes over the work of coordination, planning, and execution.
Your first question might be; is this not what AI systems already propose to do? Do the same challenges that prevent full AI automation now not also apply to Agentic AI? And you’d be right to ask.
Agentic AI differs from these in the scale of its implementation. Agentic AI systems automate business processes, one workflow at a time. It brings together a range of different AI systems to deliver complete automation. There’s no single technological breakthrough that makes Agentic AI possible; it’s more like a philosophy of Intelligent Automation.
But Agentic AI also operates on the customer side. Mass market AI assistants, such as Apple Intelligence, aim to operate as digital agents on behalf of customers; able to complete online tasks, such as interacting with digital shopfronts, booking appointments, and liaising with organizations. Even if you’re not implementing Agentic AI systems, you may still need to interact with them as customers.
What Analysts Say About Agentic AI
The idea of Agentic AI is radically new. It took everyone by surprise, including leading industry analysts, most of whom hadn’t heard the term until it began popping up in their LinkedIn comments.
But analysts were quick to look into the issue. Leaders like Gartner and Forrester included Agentic AI in their predictions for 2025, and offered some key insights into the potential of the technology:
These predictions prove that Agentic AI has legs. But that doesn’t mean it won’t have to jump some hurdles. Full automation is the Holy Grail for businesses looking to improve efficiency, but regulation, cost, and the limits of technology consistently stand in the way. Agentic AI doesn’t seem to offer any insight as to how these challenges will be overcome. That responsibility falls to businesses themselves, and their AI partners.
Implementing Agentic AI Right Now
Our wildest Agentic AI fantasies are still many years away. Full automation is an exciting but far-off goal. That doesn’t mean we can’t take steps to kick-start AI transformation, though.
More than any one specific technology, Agentic AI represents a revolution of process. It all comes down to the philosophy of Intelligent Automation – that is, applying the right mix of AI technologies to the right workflows, to deliver powerful automation within the limits of the possible.
And we don’t even have to wait until 2028. There are examples of Agentic AI at work right now:
- AI can take work off the plates of supervisors, predicting contact center demand, factoring in agent preferences, and generating automatic contact center schedules at the click of a button. A flexible interface means that, where necessary, the supervisor can take manual control of scheduling processes.
- Agentic AI can automate post-call tasks; leveraging LLMs to generate interaction summaries, pre-fill post-call forms, and cut nearly 30% off total Average Handling Time. All the agent has to do is review this data before approving it.
- AI can drive comprehensive reporting and feedback. It can collate relevant metrics from across the contact center, converting those into accessible summaries based on real-time sentiment analysis. Performance summaries can also be generated at the individual level for agents; giving each agent the equivalent of a personal performance coach.
Agentic AI is all about automating the contact center, one workflow at a time. There’s no magic bullet for AI adoption, though. Each of these workflows requires a delicate mix of AI technologies. Building and maintaining a solution like this requires deep technical expertise. The best way to tackle this is to bring an AI partner on board.
