Designing and Implementing an Agentic Multichannel AI System for Autonomous Customer Service Operations

This podcast presents a framework for developing an Agentic Multichannel AI Solution (MAS) that combines orchestration, natural language understanding, automation, and human-AI collaboration to revolutionize customer service. The solution enables autonomous handling of interactions, multi-language capability, and integration with enterprise applications, aiming for faster resolutions, cost savings, and enhanced customer satisfaction. A multichannel agentic AI solution for customer service desks was designed to handle voice calls, chat, and email simultaneously. The architecture features an orchestration layer that integrates with CRM/ERP systems, cloud databases, and incident management tools for automated workflows.

Building a multichannel agentic AI solution requires careful planning, from requirements analysis to phased deployment, ensuring integration with cloud systems and incident management for autonomous operations.

By addressing multilingual needs and prioritizing scalability, organizations can transform customer service, reducing costs while boosting satisfaction. This approach, grounded in real-world scopes and current technologies, empowers service centers with AI that listens, learns, and acts—delivering intelligent, consistent engagement. Future advancements will further enhance these capabilities, making agentic AI indispensable.

For More Details or to Schedule a Demo: Please contact us at Hello@TheAgentics.co.

To read the detailed case study: https://theagentics.co/insights/case-study-designing-and-implementing-an-agentic-multichannel-ai-system-for-autonomous-customer-service-operations

Multi-Agent Orchestration – Playbook for Dummies," authored by Nishith Srivastava (Nish) and posted on Agentic Commerce, detailing the concept and massive market growth of multi-agent orchestration. This technology is described as a "game changer" that coordinates multiple specialized AI agents, acting as a conductor for various systems to achieve a shared objective and overcome the limitations of traditional, siloed AI. The post outlines the benefits, including improved efficiency and cost savings, and provides extensive industry-specific use cases across sectors like Retail, Healthcare, and BFSI. Ultimately, the source serves as both an informative guide on implementation best practices and a promotional piece for The Agentics Co., an organization that offers AI-Powered Growth Execution services to help enterprises adopt AI-native solutions.

Topics covered:

• What is Multi-Agent Orchestration?
• Why is Multi-Agent Orchestration a Game Changer?
• Industry use cases of Multi-Agent Orchestration (CPG, Retail, FMCG, DTC, Healthcare, BFSI)
• Step by step process of Implementation for Multi-Agent Orchestration
• Ideal Tech Stack for Multi-Agent Orchestration
• Agentics' perspective on the impact of Multi-Agent Orchestration
• Key Performance Indicators
• Conclusion: The Future of Multi-Agent Orchestration

For details visit: https://theagentics.co/insights/multi-agent-orchestration-playbook-for-dummies

Self-Replicating AI Agents - The Rise of AI That Builds AI

Let’s dive into something that sounds straight out of a sci-fi flick but is very much a reality today: self-replicating AI agents. I’m thrilled to unpack this topic for you in a way that feels like we’re just chatting over coffee.

Imagine an AI that doesn’t just follow instructions but improves itself, creates its own successors, and even builds specialized sub-agents to solve problems. It sounds like sci-fi, but it’s already happening in labs and, increasingly, in real-world business applications. So, what are these agents, how do they work, and why are companies across industries buzzing about them?

Let’s break it down, explore real-world examples, and peek into their potential to shake things up in sectors like retail, healthcare, and more.

Topics covered:

• What Are Self-Replicating AI Agents?
• How Do They Work?
• How Are Companies Adopting These Agents?
• Prominent Use Cases and Real-World Examples
• Potential Applications and Future Uses
• Prominent Case Studies and Their Impacts
• Industry-Specific Implementation Blueprints
• How to Integrate Self-Replicating AI - Building Blocks andIntegration
• Executive Decision Framework
• Final Thoughts: The Genie is Out

To read the detailed article, please visit: https://theagentics.co/insights/self-replicating-ai-agents-the-rise-of-ai-that-builds-ai

Generative Engine Optimization (GEO) as an evolution in digital marketing, is distinguishing it from traditional Search Engine Optimization (SEO) and Answer Engine Optimization (AEO). GEO focuses on optimising content for AI-driven search engines like ChatGPT and Google's AI Overviews, aiming for content to be cited and synthesised into comprehensive AI responses rather than merely ranking in search results. The podcast explains how GEO differs from AEO, highlighting GEO's broader goal of influencing AI-generated narratives through authoritative and well-structured content. It also provides strategies for integrating GEO into marketing, emphasising quality, E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness), conversational language, and regular updates, alongside case studies illustrating its benefits for increased reach and brand authority in an evolving AI-centric search landscape.

Topics Covered in Detail:

• What Is Generative Engine Optimization?
• How Is GEO Different from Answer Engine Optimization?
• How Marketers and Companies Can Integrate GEO?
• Solution Approach: Crafting GEO-Friendly Content
• Strategies for Implementing Generative Engine Optimization
• Market Trends, Forecast and Use Cases & Case Studies
• 
  

Why is Agentic Commerce gaining such traction at this specific moment? The recent, mind-blowing leaps in “GenerativeAI” and “Large Language Models (LLMs)” have dramatically improved AI's ability to understand natural human language. This sophistication means AI can now "get" what we truly want, even if our instructions are nuanced or incomplete. Agentic commerce is the ultimate answer to this craving, promisingto make shopping practically effortless. Add to this the increasing interconnectivity of digital applications and a maturing data infrastructure, and you have the fertile ground necessary for these intelligent agents to thrive.

Podcast Covers:

• Deconstructing Agentic Commerce: How It Truly Functions
• Integrating Agentic Commerce: Blending with Your Existing Tech
• Integrating with the Broader IT Ecosystem: Beyond the Storefront to Legacy Ecosystem
• Integrating with Your Martech Stack: The Personalization Engine
• The Transformative Benefits of Agentic Commerce
• Elevating the Customer Experience (CX): The Ultimate Goal
• Driving Business Growth & Efficiency: The ROI Story with Agentic Commerce
• Navigating the Path to Integration: A Strategic Roadmap for Agentic Commerce Implementation
• Best Practices and Overcoming Challenges for a Successful Implementation of Agentic Commerce
• Confronting the Challenges: A Realistic Outlook
• Market Insights and Future Projections for Agentic Commerce
• Real-World Applications: Brands Reaping the Benefits (Case Studies)
• Conclusion: Embracing an Autonomous Future

Interview -Podcast

Optimising for AI searchHow to master the shift from SEO to Answer Engine Optimisation (AEO)…Interviewed by Rodrigo Wielhouwer, Founder, Datafeed.

Topic: How AI is changing consumer search behaviour: with Nishith Srivastava

Consumer behaviour is undergoing a significant transformation, particularly in terms of how people discover products and services. Gartner forecasts a 25% decrease in traditional search engine volume by 2026, largely driven by the rise of AI chatbots. This shift in searches towards AI-powered platforms, demands a new approach to visibility and engagement from marketers.

To gain a deeper understanding of these changes, we asked Nishith Srivastava, VP, Europe - BORN (Digital, CX, Martech, Data and eCommerce Transformation) at Tech Mahindra, with over 25 years of experience in omnichannel customer experience, MarTech architecture, data-driven growth and marketing strategy across global markets, about the implications of this shift for marketers, he emphasised the critical need for adapting to new consumer interactions shaped by AI:

“Users no longer 'search' in the traditional sense, they 'ask.' And that pivot demands a new kind of strategy i.e. Answer Engine Optimisation (AEO). It’s not just about ranking anymore; it’s about responding. ”

In practical terms, Nishith suggests: “Marketers must shift from 'ranking' to 'resolving' because if you’re not optimising for answers, you’re invisible. AEO flips the SEO playbook. Instead of optimising for keywords, we must optimise for answers. You must rethink how content is structured, semantically tagged, and contextually served to voice assistants, generative AI models, and zero-click platforms. This means investing in structured data, schema markup, and a laser-sharp focus on entity-based content modelling.”

A critical step, Nishith advises, is to “own the question space.” This means thinking from the user's perspective rather than the marketers. Instead of assuming consumers will ask explicitly about a brand or its offerings, Nishith notes that “the majority of users have an information need, and that's the sweet spot.” Marketers should thus shift the narrative from brand-centric to user-centric, highlighting benefits and purposeful information tailored to the consumer's intent.

“Change your AEO narrative from ‘About your Brand’ to ‘Benefits and Purpose for the User.”

Additionally, Nishith stresses, “If your content isn’t built to answer, it’s built to be ignored.”

To effectively compete in this new landscape, marketers should regularly audit their content for clarity and relevance in question-and-answer formats. Tools like AlsoAsked or AnswerThePublic can be valuable to uncover and identify latent semantic queries relevant to industry-specific topics. Accordingly, structure FAQ’s and responses in concise, scannable formats (bullet points, tables, or step-by-step guides) to align with answer engines' preference for snippet-ready content.

To ensure comprehensive coverage, marketers should also map audience intent across all stages of the consumer journey, aligning content closely with conversational queries. Nishith recommends prioritising conversational phrases such as 'how', 'why' and 'when', rather than limiting oneself to just 'what'. By integrating FAQ schema, leveraging knowledge graph relationships and employing natural language generation (NLG), businesses can efficiently scale authoritative answers that AI tools trust and value.

Finally, Nishith emphasises the importance of adopting a conversational tone. He points out that voice search and AI assistants prioritise natural language and suggests claiming your knowledge graph real estate with structured data (Schema markup) to reinforce entity-based authority.

By proactively embracing these strategies, marketers can successfully navigate and thrive in the evolving landscape of AI-assisted consumer discovery.

To read more: https://www.datafeed.website/post/optimising-for-ai-search

This podcast introduces Answer Engine Optimization (AEO) as a critical digital strategy for the age of AI-driven search. It explains that unlike traditional SEO focusing on broad keyword ranking, AEO aims to position content to provide direct answers to user queries through platforms like Google SGE, Microsoft Copilot, and AI chatbots.

The podcast outlines key strategies for implementing AEO, including optimising content for voice search, using structured data markup, and creating comprehensive FAQ pages, emphasizing that this approach enhances online visibility, user engagement, and organic traffic by establishing brand authority in the evolving search landscape.

Topics Covered:

• Whats is Answer Engine Optimization?
• Why is AEO becoming increasingly important?
• How AEO differs from traditional SEO - Future of SEO is AEO
• Types of Answer Engines
• The Functioning of Answer Engines – How do you optimize for Answer Engines?
• Benefits of AEO for businesses

This podcast discusses Vibe Marketing, a modern approach that leverages AI tools and automation to significantly enhance marketing efficiency and effectiveness. It explains that vibe marketing allows a small team, or even a single marketer, to perform complex tasks like rapid ad testing, multi-channel campaign deployment, and data analysis, which traditionally required numerous specialists.

The podcast contrasts vibe marketing with traditional methods, highlighting its speed and emphasis on AI handling execution while humans focus on strategy and brand voice. Several examples of brands successfully using vibe marketing are provided, alongside actionable steps and specific AI tools to help businesses implement this approach. Ultimately, the source posits that vibe marketing is a crucial evolution in the industry, offering cost savings, increased output, and a stronger focus on building authentic brand connections.

Topics Covered:

• Vibe Marketing for Dummies
• Market Forecast
• Vibe Coding vs. Vibe Marketing
• AARRR Pirate Funnel
• Examples Of Vibe Marketing – Use Cases from Brands
• How Can Your Business Implement Vibe Marketing?
• Various AI Tools for Vibe Marketing Future of Vibe Marketing