The What, Why, and How of Agentic AI: Moving Beyond Generative AI for Transformative CX

"Agents are not only going to change how everyone interacts with computers. They're also going to upend the software industry, bringing about the biggest revolution in computing since we went from typing commands to tapping on icons." — Bill Gates

AI in CX is no longer a futuristic experiment, but a strategic necessity!

The evolution from Generative AI to Agentic AI

Over the past few years, Generative AI has fundamentally transformed Customer Experience (CX) by enabling enterprises to produce human-like text, images, and even code. Businesses quickly harnessed these capabilities to automate content creation, personalize interactions, derive actionable insights, and speed up decision-making.

However, despite its transformative capabilities, GenAI has in real-world enterprise applications. Models like GPT, while powerful, lack real-time, context-specific knowledge, leading to challenges such as hallucinations, context-limited responses, and restricted decision-making capabilities. To mitigate these, we adopted techniques like Retrieval-Augmented Generation (RAG), function calling, and prompt engineering.

Yet, even with these enhancements, a fundamental gap remained: the ability to reason, strategize, and autonomously act in complex business environments. This is the gap that Agentic AI is designed to close.

Beyond Generative AI: The emergence of intelligent, action-oriented agents

To bridge these gaps, a new generation of models has been introduced in recent months. Reasoning models emerged, capable of breaking down complex problems, strategizing workflows, and deriving efficient solutions. Additionally, autonomous agents/action models (e.g., Anthropic’s computer-use models or browser-use models like Operator) introduced perception, reasoning, and—most importantly—autonomous execution capabilities.

With the convergence of stronger generative, reasoning, and action models, we are no longer limited by AI’s previous constraints. Welcome to Agentic AI—where AI evolves from a passive assistant to an active problem-solver!

Agentic AI surpasses traditional Robotic Process Automation (RPA) by combining natural language processing, advanced reasoning, and actioning capabilities while allowing human oversight for complex decision-making, unforeseen scenarios, and creative problem-solving.

For example, competitive intelligence is a time-consuming process typically conducted quarterly on a select few competitors due to resource constraints. With Agentic AI, enterprises can automate this process end-to-end, expanding analysis to weekly intervals and covering a broader competitor landscape, ultimately enhancing market intelligence, strategic decision-making, and revenue growth.

Debunking the myth: Will Agentic AI replace human jobs?

A frequent concern among business leaders and employees alike is whether Agentic AI signifies the end of certain job roles. The short answer is—no, not directly.

Agentic AI augments human capabilities, not eliminates them. The newer agentic models allow us to automate repetitive, well-defined processes, but most roles involve a mix of high-judgment, non-repeatable tasks that frequently change—which are neither cost-effective nor practical to automate entirely. Instead, Agentic AI allows teams to focus on strategic, high-value work while AI handles repetitive, time-consuming tasks.

Why should enterprises embrace Agentic AI?

Agentic AI is not merely another AI advancement—it’s a competitive differentiator. It has implications across multiple enterprise functions, from marketing and operations to product management and customer support. Key benefits include:

• Efficiency gains: Automate resource-intensive, repetitive processes, significantly improving productivity.
• Enhanced decision-making: Deliver timely, precise insights through context-aware analysis, enabling faster, more informed strategic decisions.
• Superior CX: Personalize and enrich customer interactions at scale with real-time data-driven insights, improving satisfaction and retention.
• Revenue growth: Leverage increased operational efficiency and strategic responsiveness to drive incremental revenue.
• Competitive edge: Businesses slow to adopt Agentic AI risk lagging behind competitors that deliver superior experiences through advanced automation and decisioning capabilities.

How to successfully adopt Agentic AI?

1. Understanding AI agents

An AI agent is an entity that analyzes, makes decisions and executes actions based on provided instructions. To function effectively, an Agent requires:

• A Large Language Model (LLM): A foundational AI model (generative, reasoning, or action-oriented).
• Instructions: Well-defined guidelines directing the agent’s decisions and behaviors.
• Knowledge base: Leveraging internal data (e.g., SharePoint, internal databases) or external data from the web.
• Action layer: Enabling real-world interactions, such as sending emails, updating CRM records, or triggering marketing campaigns.

2. Orchestrating AI agents for maximum impact

Automating a single task is useful, but to fully realize Agentic AI’s potential, enterprises must orchestrate multiple specialized agents into cohesive workflows - each agent executing tasks aligned with its core strengths (such as web browsing, data analysis, content generation, or executing actions), and collectively, they automate entire business processes.

To accomplish this, a robust multi-agent orchestration framework becomes critical here, providing temporary (ephemeral) memory and persistent storage for context-sharing and continuity across complex workflows. The modularity of this approach enables organizations to reuse agents as microservices, ensuring flexibility, scalability, and optimal use of resources.

Consider the earlier competitive intelligence example: effective implementation requires orchestrating specialized agents such as a Web Surfer (action-driven browser interactions), Perceptor (generative content summarization), Strategizer (reasoning-based strategic recommendations), Activator (execution of marketing actions), and Validator (quality assurance through analytical validation). Together, they form a comprehensive, efficient, high-quality, automated intelligence-gathering process.

3. Building enterprise-ready AI agents

Building enterprise-ready Agentic AI demands robust multi-agent orchestration framework. Fortunately, we don’t have to build from scratch and the market offers ecosystems that help meet these requirements. For example:

• Semantic Kernel/AutoGen: Microsoft framework for orchestrating AI agents built with Azure AI Foundry and Azure AI Agent Services SDK.
• Amazon Bedrock: A fully managed service within AWS to build and orchestrate AI agents with in-built tools like Agents, Flows, etc.
• Google AgentSpace: Google’s AI Agent environment for building agents(in early access); Vertex AI Agent Builder enables smaller-scale agent orchestration

Apart from the above, there are many other agent orchestration solutions from OpenAI, Postman, Asana, Workday, ServiceNow, etc. Selecting the right platform depends on an enterprise’s existing tech stack, data strategy, and AI objectives.

4. Experimenting and scaling Agentic AI quickly

For enterprises looking to implement Agentic AI, a phased approach ensures success:

• Identify high-impact use cases: Prioritize labor-intensive, high-value business processes (e.g., marketing automation, competitive intelligence).
• Conduct pilot projects: Build agents for a low-risk, high-impact use case and validate outcomes with measurable KPIs.
• Scale and optimize: Gradually expand use cases and integrate learning through feedback loops to maximize ROI.

Quantifying the ROI of Agentic AI

Implementing agentic AI is not just about innovation—it’s about measurable business impact. To ensure success, identify the right use cases based on repeatability, frequency, business impact, and labor intensity. A standardized method to calculate ROI from Agentic AI adoption can be structured as follows:

Annual Cost Savings = (Manual effort hours × Hourly labor cost × Annual frequency)

− [ (AI licensing + Hosting + Operational costs) + (Human-in-the-loop Hours × Hourly Labor Cost × Annual frequency) ]

ROI (%) = ((Annual Cost Savings + Projected revenue increase from improved speed, frequency, and quality (based on historical data))−Total automation investment )×100/Total automation investment

This data-driven approach ensures that investments in agentic AI are strategically aligned to deliver tangible benefits.

The future: Every enterprise will have an Agentic AI framework

Just as having a digital presence became fundamental for businesses, an Agentic AI framework will soon become indispensable for maintaining a competitive edge. Organizations that proactively embrace this shift today will gain significant advantages in efficiency, agility, and customer experience tomorrow. By automating routine processes, businesses can redirect time and resources toward strategic, non-automatable initiatives.

Now is the time to embrace the AI revolution in CX – with the right partner and plan, you can turn this cutting-edge capability into your next engine of growth. At Altudo, we help enterprises onboard Agentic AI quickly and effectively, leveraging the right platforms, orchestration frameworks, and AI models. Our proven workshop approach identifies high-value use cases, accelerates experimentation, and structures investments for rapid ROI.

Let’s transform your CX together, making your AI vision a reality!