Silverback AI Chatbot has unveiled the latest development in its platform with a continued emphasis on AI Agents, strengthening their role as a foundation for business process automation and customer interaction management. The update reflects how artificial intelligence is progressing from simple chat applications into systems that execute structured, outcome-driven tasks with persistence and autonomy.
AI Agents are increasingly viewed as the next stage in AI-powered interaction. Unlike traditional chatbots, which primarily respond to individual prompts, agents are designed to complete multi-step processes while retaining context across multiple conversations. Silverback’s implementation of this technology integrates natural language processing, task management frameworks, and system-level integrations to create agents capable of functioning as digital counterparts to structured business functions.
The system enables agents to handle workflows that require continuity and decision-making. Examples include qualifying leads, scheduling appointments, updating customer databases, or providing support across several days of communication. By combining memory modules, secure APIs, and large language models, the AI Agents operate with a level of independence that reduces the need for repeated human intervention during routine interactions.
One of the key distinctions in Silverback’s approach is persistence across communication channels. A conversation may begin on a company website, continue on a messaging platform, and conclude through email, with the AI Agent maintaining full awareness of context and task progress. This capacity addresses a longstanding challenge of earlier chatbot systems, which were unable to manage workflows that extended beyond a single interaction or session.
The AI Agents framework has been built to accommodate businesses that do not have dedicated AI development teams. Through accessible configuration tools, organizations can define agent goals, connect them to existing software systems, and set parameters for behavior. This accessibility allows small and mid-sized businesses to deploy automation that previously required custom development.
Use cases for the agents span a wide range of industries. A healthcare provider might implement an AI Agent to manage appointment reminders, patient intake forms, and follow-up communications. A real estate agency could configure agents to qualify potential buyers, collect preferences, and arrange property viewings. Retailers, meanwhile, may deploy agents to process product inquiries, provide order status updates, and handle return workflows.
As AI Automation becomes more deeply embedded in operations, data privacy and regulatory compliance remain a central concern. Silverback AI Chatbot has emphasized safeguards, including encryption of inputs, audit trails, and business oversight tools that provide visibility into agent decision-making. These controls are designed to balance automation with accountability and trust.
The AI Agents system also incorporates a feedback-driven improvement cycle. Businesses can review metrics such as task completion rates, engagement outcomes, and customer satisfaction levels. These insights allow workflows to be refined over time, ensuring that the agents evolve in performance and continue to meet business objectives as requirements change.
This release arrives during a period of increasing adoption of automation technologies across industries. With organizations seeking to maintain service consistency while operating under leaner staffing models, AI Agents offer a means to scale operations without proportionally increasing human resources. Analysts have noted that this approach signals a shift in strategy, with businesses treating AI not just as a conversational interface but as an operational layer capable of executing defined objectives.
Future directions for the system may include expansion into internal business functions. While initial applications emphasize external engagement—such as customer service and lead management—the architecture also supports scenarios like HR onboarding workflows, internal IT support, and automated reporting. Silverback has indicated that these opportunities are being explored in response to user feedback.
Supporting resources have been released alongside the update to help organizations deploy AI Agents effectively. These include workflow templates, user documentation, and guidance materials for non-technical stakeholders. The goal is to simplify adoption and ensure that the benefits of intelligent automation are available to a wide spectrum of businesses.
Industry observers describe the rise of AI Agents as a critical step forward in practical artificial intelligence applications. By focusing on outcomes rather than isolated exchanges, these systems represent a shift toward AI that acts as an operational partner. Silverback AI Chatbot’s implementation reflects this evolution, highlighting the role of agents in achieving continuity, efficiency, and structured task execution at scale.
As businesses continue to explore AI-driven strategies, the integration of AI Agents into mainstream workflows underscores both the opportunities and responsibilities of automation. Ensuring security, oversight, and ethical use will remain as important as technical capability in shaping adoption trends.
With the release of its expanded AI Agents framework, Silverback AI Chatbot positions itself within this emerging landscape, delivering a model of automation that is both adaptable and accessible. The system demonstrates how AI can progress beyond simple chat toward agent-driven execution of real business outcomes.
The Dr. Pritam Singh Foundation, in collaboration with IILM University, hosted a discussion on “Human at Core: AI, Ethics, and the Future” at Tech Mahindra, Cyberabad, on Saturday, in memory of the late Dr. Pritam Singh, a noted academic.
After launching the discussion, Assembly Speaker Gaddam Prasad Kumar highlighted the ethical challenges of Artificial Intelligence (AI), warning against algorithmic bias, threats to data privacy, and job displacement. He called for large-scale reskilling and emphasised that India must shape AI technologies to reflect its values of fairness, transparency, and inclusivity. He urged corporate leaders to establish strong governance frameworks, audit algorithms for bias, and ensure responsible adoption of AI.
Delivering the keynote address, Chairman of Administrative Staff College of India (ASCI) K. Padmanabhaiah stressed India’s opportunity to leverage AI for inclusive growth across healthcare, agriculture, education, and fintech — while ensuring technology remains human-centric and trustworthy.
One of the founders of the Dr. Pritam Singh Foundation P. Dwarakanath, Director at IILM University Chaturvedi, Director at the Institute for Development & Research in Banking Technology (IDRBT) Deepak Kumar, Managing Director of Signode Asia Pacific Gaurav Maheshwari, Pritam Singh’s son Vipul Singh, and author and economist Vikas Singh spoke.
With fears about the strength of consumer spending running high due to tariffs, inflation and other economic pressures, retailers are working hard to sustain revenue growth. While some retailers are leaning into worker-led personalized experiences for shoppers, other retailers are focusing more on leveraging artificial intelligence to optimize the shopping experience.
Walmart is one of those retailers, adding new “super agents” that aims to save time and effort for both workers and shoppers. At its recent Retail Rewired innovation event, Walmart highlighted the launch of four “super agents,” which include Marty for sellers and suppliers, Sparky for shoppers, the Associate Agent and the Developer Agent.
With agents performing capabilities in the realm of payroll, paid time off, merchandising and finding the right products for any event, Walmart is consolidating its powerful, time-saving tools for the sake of a streamlined experience for multiple points of interaction with the company.
“Having a plethora of different agents can very quickly become confusing,” Suresh Kumar, chief technology officer for Walmart Global, said at the event.
The Associate Agent, for example, is “a single point of entry where any associate can find access to all of the agents we’ve built on the back end,” explained David Glick, senior vice president for Enterprise Business Solutions at Walmart. “As you speak to it more, as you work with it more, it’ll know more about you.”
The evolution comes alongside a broader shift for retail, an industry actively seeking to counteract cost concerns from consumers and the government, and Walmart isn’t alone in its push toward all things AI. Amazon’s Prime Day event over four days in July saw generative AI use jump 3,300% year over year, according to TechCrunch. Meanwhile, Google Cloud AI partnered with body care retailer Lush to visually identify projects without packaging, ultimately reducing the expense of training new hires.
Making digital twins of Walmart stores
Walmart is also all-in on physical and spatial AI, specifically digital twins (a virtual copy of any physical object or space — in Walmart’s case, their stores and clubs). Using digital twin technology powered by spatial AI, Walmart can “detect, diagnose and remediate issues up to two weeks in advance,” Brandon Ballard, group director for real estate at Walmart US, said at Retail Rewired. Using this technology comes with big savings, according to Ballard. “Last year, we cut all of our emergency alerts by 30% and we reduced our maintenance spend in refrigeration by 19% across Walmart US,” he added.
“At its core, retail is a physical business,” said Alex de Vigan, CEO and founder of Nfinite, which generates large-scale visual data for training spatial and physical AI models. “We’ve seen retailers use digital twins to reduce setup time for new promotions, reallocate labor more efficiently, and improve robotic picking accuracy, small gains that add up quickly when margins are under stress,” he said.
While the impact of digital twins may not be outwardly visible to consumers in the same way, say, Walmart’s Sparky agent is, its effects will be real. “Better stock accuracy, faster site updates and fewer order issues mean a smoother retail experience, even in a tighter economy,” said de Vigan.
Another innovation on the back end is Walmart’s use of machine learning to better understand how long it will take to get a delivery order on a customer’s doorsteps, effectively managing expectations while increasing efficiency.
As for what consumers can see, Sparky is already helping shoppers generate baskets built on an intuitive understanding of their needs. Walmart is currently working on enabling the agent to take action on reordering products, ultimately reducing the mental load that shoppers deal with.
For retailers, AI is one way to combat any slowdown in consumer spending, but we’ve yet to see how a fully integrated AI shopping experience — both in person and online — will shape our relationship with retail moving forward.
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Amid the hype over AI, a practical question: When will the technology boost the economy the way its developers and promoters are promising? Is artificial intelligence going to unleash a surge in worker productivity, as epochal new tech has done in the past? Or is investor enthusiasm for it overdone?
In one sense, AI is already adding to GDP. Spending on AI hardware is astronomical, both for the costly, specialized chips that power AI and the related infrastructure to deliver the electricity that those chips devour. This spending raised GDP by 0.3% in the second quarter of this year. Even that doesn’t fully capture the size of this investment surge, since some capital outlays that tech firms are making don’t show up in the official GDP accounting method. Just look at the top five firms by AI investment: Amazon, Alphabet, Meta, Microsoft and Oracle. The increase in their AI-related capital expenditures over the past two years equals about 10% of GDP gains in the U.S. over that time period. Add the power plants, transmission lines and other infrastructure they need to run their data centers, and the outlay is even bigger.
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There are also signs that businesses are gearing up for AI to make an impact on their operations. Company mentions of AI use for research tripled since Nov. 2022, when ChatGPT launched and turbocharged generative AI. 25% of job listings posted for IT professionals since the start of last year have asked for AI-related skills. The number of mobile AI app downloads hit 60 million this March. Internet searches related to AI have grown tenfold since OpenAI unveiled ChatGPT to the public. When it comes to whether AI will make workers more productive, the picture gets murkier. There are some early signs that it’s happening. Inflation-adjusted revenue per worker among S&P 500 companies has been rising since late 2022, following a 15-year period when it stayed flat.
It’s not clear why, but the overlap with advanced AI applications going mainstream is hard to ignore. But with so much money pouring into AI, there are reasons for skepticism. Much of the investment being made today could end up wasted. Many companies that are in vogue now figure to fail. It’s possible that AI computing power being rushed online could ultimately prove to be unneeded, akin to how fiber-optic cable networks got overbuilt in the 1990s. That capacity eventually got used as data consumption rose, but not before builders who spent too much on it went bankrupt. If the current AI data center boom fizzles, the pullback in spending could spark a mild recession, as the tech bust in 2001 did. Most major technological leaps take time to filter through the economy. AI does seem genuinely transformative. But the transformation may take many years.
This forecast first appeared in The Kiplinger Letter, which has been running since 1923 and is a collection of concise weekly forecasts on business and economic trends, as well as what to expect from Washington, to help you understand what’s coming up to make the most of your investments and your money. Subscribe to The Kiplinger Letter.
