Agentics presents its latest podcast that provides quick introduction on Artificial General Intelligence (AGI) for beginners. Defining it as the critical technological leap from specialized narrow AI to systems capable of generalized, human-level reasoning and adaptation across any intellectual task, the podcast chronicles the massive investment surge into AGI research, driven by key players like OpenAI, Google DeepMind, and NVIDIA, noting that the small AGI market is projected to skyrocket to over $11 billion by 2032.
Mechanically, AGI development focuses on achieving transfer learning, emulating human cognition (System 1 and System 2 thinking), and ensuring autonomy, moving beyond the current limits of large language models like GPT-5.
While experts like Sam Altman and Elon Musk predict AGI within the next few years, the median expert forecast places the 50% chance of human-level AI closer to 2040, emphasizing the high stakes surrounding ethical governance and safety. Ultimately, the material frames AGI as an exponential breakthrough that promises radical societal abundan
Large Language Models (LLMs) are trained on a predominantly Western corpus, leading to cultural biases that can hinder their effectiveness and adoption in non-Western contexts. To address this, LLMs must be adapted to regional nuances, incorporating local languages, idioms, and cultural references. This involves fine-tuning models on region-specific datasets, integrating behavioral insights, and establishing governance frameworks to ensure ethical and safe AI use.
Large Language Models (LLMs) are often trained on data that reflects Western perspectives, leading to biases and cultural insensitivity. To address this, a three-layered approach is proposed: culturally curated data, behavioral insights, and region-specific governance models. This approach aims to create AI that understands and respects local cultures, values, and communication styles, ensuring global adoption and avoiding cultural erasure.
Read more: https://theagentics.co/insights
Case Study: Agentic eCommerce - Competitor Price Intelligence & Dynamic Pricing Suggestions
A leading consumer brand in India’s home appliances sector faced challenges maintaining market share due to competitor price undercutting. Agentics deployed an AI-driven competitor monitoring and pricing recommendation engine, providing real-time insights into competitor pricing dynamics and actionable pricing suggestions. The solution resulted in a 15% uplift in e-commerce sales volume, improved profitability, and a more agile pricing strategy.
Agentics' self-optimizing AI ensures that every pricing decision refines future recommendations, building a sustainable competitive edge. In India's dynamic home appliances market, where competitor intelligence is now a necessity rather than an advantage, this approach drives long-term growth and resilience.
To read the detailed case study: https://theagentics.co/insights/case-study-agentic-ecommerce-competitor-price-intelligence-dynamic-pricing-suggestions
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.
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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.
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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:
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.
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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.
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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.
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AI agents are software tools that act autonomously to perform tasks, leveraging AI models, particularly large language models, to understand language and interact with systems. These agents can automate workflows, augment individual productivity, and even form complex, collaborative systems within organisations.
Recent advancements have enabled agents to move beyond knowledge retrieval to action-oriented capabilities, leading to their potential widespread adoption across various industries. The podcast covers different types of AI agents, their operational processes, and their relationship with LLMs, further exploring their potential to drive business growth by reimagining processes and modernising infrastructure.
Organisations face hurdles in adoption, including building trust, managing change, and ensuring data protection, while their tech architectures will likely evolve towards multiagent models. Ultimately, the implementation of AI agents requires strategic planning in technology, talent, and operating models to unlock their transformative potential.
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What vibe marketing adds is a framework to harness this automation not just for efficiency, but for exponential growth.Vibe marketing is a significant evolution in marketing, driven by artificial intelligence and accessible automation, promising faster campaign cycles and greater efficiency. This new approach empowers marketers to achieve exponential growth by automating repetitive tasks and enabling rapid, large-scale testing.
The podcast contrast traditional, siloed marketing teams with the agility offered by vibe marketing, where a single marketer can leverage AI tools to manage complex campaigns.
Real-world examples illustrate its application, and the text suggests that embracing vibe marketingoffers a crucial competitive advantage in the rapidly evolving digital landscape.
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Agentic AI, a form of artificial intelligence capable of autonomous action and decision-making, is poised to significantly reshape the healthcare industry. This podcast highlights how Agentic AI can enhance diagnostic accuracy, personalise treatment plans, and streamline administrative tasks, ultimately leading to improved patient outcomes and reduced costs.
Furthermore, it discusses the potential of agentic AI to accelerate drug discovery and improve access to healthcare, particularly in underserved areas. The podcast also addresses crucial challenges for successful implementation, such as data quality and the need for strategic integration, offering solutions like centralised governance and a focus on measurable value.
Agentic AI: Transforming B2B Ecosystems in Key Industries
This podcast explores the burgeoning field of agentic artificial intelligence (AgenticAI) and its transformative impact on business-to-business (B2B) ecosystems, particularly within pharmaceuticals, industrial manufacturing, chemicals, and oil & gas.
It highlights the projected substantial growth of this market and contrasts agentic AI with generative AI, emphasising its specialisation in vertical-specific applications using proprietary data.
It discusses various use cases across these industries, illustrating how agentic AI enhances efficiency, optimises workflows, and improves decision-making through technologies like AI, IoT, and digital twins, ultimately leading to more proactive and personalised B2B interactions.
By exploring real-world use cases, it demonstrates how agentic AI drives efficiency, innovation, and transformation in these sectors. The discussion highlights the strategic integration of intelligent systems to optimize B2B workflows and enhancedecision-making by providing the overall technology stack that is required to integrate Agentics AI in existing B2B ecosystem.
This podcast examines the challenges and strategies for deriving return on investment (ROI) from generative AI (GenAI) investments. It then highlights the significant economic potential of GenAI, citing projections of trillions of dollars inadded value, while also cautioning that enthusiasm currently outpaces understanding.
The podcast then delves deeper into specific challenges in measuring GenAI ROI, such as the lack of standardised metrics, the complexity of attributing impact, and the difficulty in quantifying intangible benefits. It advocates for a balancedapproach to key performance indicators (KPIs), incorporating both financial and non-financial metrics.
Furthermore, it provides a balanced approach to key performance indicators (KPIs), incorporating both financial and non-financial metrics. Furthermore, the podcast outlines a framework for setting up and measuring ROI, including defining objectives, establishing performance baselines, and continuously improving implementations; as well as the importance of addressing data complexity, ethical considerations and business environment changes.
The challenges are evident: a lack of standardization, complexities in attribution, and benefits that are often hard to quantify. However, a practical and iterative approach—guided by clear objectives, human oversight, and data-driveninsights—can unlock the full potential of generative AI.
Organizations that perceive ROI as an ongoing process, continuously refining their strategies and metrics, will be best positioned to transform AI investments into measurableimpacts.
The true value of generative AI goes beyond cost savings and efficiency gains—it lies in its ability to transform processes, stimulate innovation, and empower better decision-making. In this rapidly evolving landscape, those who succeed will bethose who redefine ROI, striking a balance between quantifiable financial outcomes and strategic, long-term contributions.
This podcast examines the progression of artificial intelligence (AI) from its current narrow applications (ANI) to the theoretical realms of Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI). It defines AGI as AI mirroring human cognitive abilities and ASI as AI surpassing human intelligence in every aspect.
It further explores the potential benefits of AGI and ASI, including advancements in healthcare, science, and problem-solving, as well as associated risks, such as loss of human control and ethical dilemmas.
It identifies key technologies that may drive the evolution towards ASI and presents existing AI applications as foundational building blocks.
Finally, it advises business leaders to proactively prepare for the disruptive potential of AGI and ASI by investing in AI literacy and embracing ethical experimentation and agile adaptation.
The era of autonomous, AI-driven commerce is here.
The question isn’t whether AI will redefine eCommerce—it already has.
The real challenge is: Are you ready to embrace it?
This podcast explores the rise of "Agentic Commerce" and its potential to transform the eCommerce landscape.
Section 1: How Agentic AI is transforming the eCommerceLandscape
- Highlights significant investments in Agentic AI by techgiants like Microsoft, Tesla, Amazon, and Google.
- Diverse applications of Agentic AI across B2B and B2Csectors, including autonomous procurement, personalised shopping assistants, and optimised fulfilment.
Section 2: How to build Agentic AI-powered eCommerceEcosystems
This podcast introduces the concept of digital twins and their growing importance in eCommerce and retail. They define digital twins as virtual representations of physical products, customers, or operational processes, and highlight their capacity to simulate behaviours and improve decision-making.
Applications span personalised shopping experiences through interactive product visualisation, enhanced product development via real-time consumer insights, and optimised supply chain management.
The authors stress digital twins' role in predictive consumer insights, sustainable practices, and potential advancements through AI, AR, 5G, and expansion into new retail sectors.
Adopting digital twins can give retail firms an early-mover advantage if implemented alongside an established digital culture to keep the twins reliable and up to date.
Agentic AI, unlike traditional AI, operates autonomously, making decisions and acting independently within defined parameters. The podcast covers: