The Rise of Agentic AI: Transforming Business Operations and Customer Engagement

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A week ago

Key Highlights:

Organizations are leveraging agentic AI to streamline operations, resulting in significant time and cost savings.
AI agents are enhancing customer engagement by improving conversion rates and allowing human employees to focus on more value-added tasks.
The complexity of managing multiple AI agents requires robust orchestration and evaluation frameworks to ensure reliability and adaptability.

Introduction

As businesses increasingly adopt AI technologies, the deployment of agentic AI—intelligent agents capable of performing complex tasks autonomously—has emerged as a transformative force. This shift is evident across various industries, where organizations are not just integrating AI for automation but are rethinking their operational frameworks to harness the full potential of these systems. During VentureBeat’s Transform 2025 conference, industry leaders discussed the current state and future prospects of agentic AI, sharing insights into how these technologies are reshaping the way companies operate and engage with customers.

The conversation highlighted both the challenges and opportunities presented by agentic AI, particularly in terms of improving efficiency, enhancing customer experiences, and navigating the complexities associated with scaling these systems. As organizations grapple with implementation, it becomes crucial to understand the practical applications, the intricacies of managing AI agents, and the strategic partnerships necessary for success.

AI Agents: Beyond Automation

The initial motivation for deploying AI agents often centers around cost reduction and efficiency. However, industry experts emphasize that the true value of agentic AI lies in its capacity to transform business processes fundamentally. Shailesh Nalawadi, VP of project management at Sendbird, noted that while saving human capital is significant, it does not fully capture the transformative potential of AI agents.

At Rocket Companies, for instance, the implementation of AI agents has led to remarkable improvements in customer conversion rates. CTO Shawn Malhotra revealed that clients engaging through AI-driven conversational experiences are three times more likely to complete a transaction compared to traditional channels. This statistic underscores the importance of providing seamless and interactive customer experiences, which AI agents can facilitate effectively.

Moreover, the ability to automate specialized tasks—such as calculating transfer taxes during mortgage underwriting—has resulted in substantial financial savings. Malhotra highlighted a case where a two-day development effort for an AI agent saved the company over a million dollars annually. This example illustrates how AI can alleviate employees from repetitive tasks, enabling them to concentrate on high-value interactions that significantly impact customer satisfaction.

Tackling the Complexity of AI Agents

As organizations integrate AI agents into their operations, they face the challenge of moving from a traditional software engineering mindset to a more probabilistic approach. This transition requires not only technical expertise but also a cultural shift within teams. Nalawadi pointed out that engineering teams must adapt to a landscape where AI models, particularly large language models (LLMs), produce varying outputs for the same queries, necessitating a different testing and quality assurance paradigm.

Thys Waanders, SVP of AI transformation at Cognigy, emphasized that the advancements in LLMs have made them more reliable than ever. However, as the volume of automated interactions grows—potentially reaching tens of millions annually—companies must also consider how to orchestrate these agents effectively. The orchestration layer plays a critical role in determining which agent handles specific customer queries based on their capabilities and the context of the interaction.

Malhotra elaborated on the implications of managing multiple agents, noting that the complexity of routing requests across a network of AI agents will present significant technical challenges in the coming years. Ensuring low latency and high responsiveness in customer interactions will be paramount, and organizations will need to develop sophisticated systems to manage these demands.

Leveraging Vendor Relationships for Success

Historically, many companies have begun their journey with agentic AI by developing in-house solutions. While this approach can provide initial insights and capabilities, it often leads to limitations as organizations attempt to scale and improve their AI systems. Nalawadi observed that successful engagements often arise from companies that have already experimented with internal builds, only to realize the necessity of specialized vendor partnerships to enhance their capabilities.

The evolving landscape of agentic AI demands that organizations not only focus on creating a viable product but also invest in ongoing development and improvement. This includes the need for expert guidance to navigate the rapidly changing technology environment and to implement best practices for maintaining and scaling AI infrastructures.

Preparing for the Future of Agentic AI

Looking ahead, the complexity of agentic AI is expected to increase as organizations deploy more agents and expand their use cases. This evolution will necessitate robust checks and balances to ensure reliability and accountability. Malhotra highlighted the importance of having human oversight in critical processes, as well as effective monitoring systems to detect issues proactively.

To build confidence in the reliability of AI agents, organizations must prioritize establishing rigorous evaluation frameworks before development begins. Nalawadi stressed the necessity of defining what success looks like for AI agents and incorporating ongoing evaluation as part of the iterative improvement process. This proactive approach helps mitigate risks associated with introducing non-deterministic behaviors inherent in AI systems.

Waanders added that simulating various scenarios at scale is essential for understanding how AI agents respond under different conditions. By pushing agents through numerous interactions, organizations can identify potential pitfalls and optimize their performance, ensuring that they meet the evolving demands of their users.

FAQ

What is agentic AI? Agentic AI refers to intelligent agents capable of autonomously performing tasks and making decisions based on data and user interactions. These agents are designed to enhance operational efficiency and improve customer engagement.

How can AI agents benefit my organization? AI agents can provide significant benefits, including cost savings, improved customer conversion rates, and the ability for human employees to focus on more strategic and value-added tasks.

What challenges do organizations face when implementing AI agents? Organizations may encounter several challenges, including managing the complexity of multiple AI agents, ensuring reliable performance, and establishing robust evaluation and monitoring systems.

How can companies prepare for the increasing complexity of agentic AI? Companies should implement rigorous evaluation frameworks, prioritize human oversight for critical processes, and invest in vendor relationships to enhance their AI capabilities and ensure ongoing adaptation to new technologies.

What role do vendor relationships play in the success of agentic AI? Vendor relationships can provide specialized expertise, access to advanced technologies, and ongoing support necessary for maintaining and scaling AI solutions effectively.

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