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Hindustan Times
6 days ago
- Business
- Hindustan Times
Agentic AI's continuous learning forms its Milky Way: Tredence's Soumendra Mohanty
Artificial intelligence (AI) in workplaces is becoming more and more common. Relevance may still vary, but there are two distinct sides to that conversation — one in which AI will take over most, if not all human jobs rendering them jobless (and possibly hopeless), whilst the other talks about the need for a great realignment. Soumendra Mohanty, who is chief strategy officer at data science company Tredence. (Tredence) Soumendra Mohanty, who is chief strategy officer at data science company Tredence, is better placed than most to decode the changes the AI space is bringing to the workplace. More so, because they design agentic AI workflows for enterprises, across multiple verticals including healthcare, banking and telecom. 'We're building agentic workflows, as well as agents and models in that area, a constellation of all of these coming into an agentic workflow where there is a human in the loop,' Mohanty gives us a glimpse into a work where more than one 'copilot' will work in sync with a human in the loop. 'And there is this feedback loop that keeps it learning is what we are calling it the Milky Way,' he adds. To add more context to the metaphor, the reference to 'Milky Way' includes building towards a constellation of agent based systems, of course the continuous learning process, the human in the loop and an interconnected network, all of which he explains in the conversation with HT. Mohanty tells us he is working on a new concept, which is AI and various avatars, when it meets the individual's ambidexterity. Edited excerpts. Q. AI agents and Agentic AI frameworks are evolving rapidly. How do you see this transformation panning out, in terms of that transition from research labs to real world? Soumendra Mohanty: This question requires elaborate context. The pace of technology and innovation over the last four or five years compared to the past few decades has been phenomenal. Compared to the previous eras of innovation was more, from very deterministic applications and systems where a user gives inputs, clicks on this or that and then it does something, to something more conversational and interaction oriented. The way humans work or interact, and that is why it is very interesting and also disruptive. In typical enterprise settings and also in our personal lives, you'll notice a human orientation and a tech orientation. The language between human orientation and if I can extrapolate that or extend that to a business orientation, and the language of tech orientation, are two different things. There is always an in-between translation and the complexity of doing it. That has always been there, but with generative artificial intelligence and conversational interfaces, that boundary has become pretty much non-existent in a sense. Of course, under the hood you will need a lot of integration, a lot of technology and heavy lifting. But on the surface, where basically the needs of humans and enterprises meet technology, that is now very seamless. It is voice enabled, gesture enabled, and natural language enabled. It abstracts the entire complexity of things The other aspect, important to understand, would be that humans have cognitive limitations. We can only process a certain amount of data and information to make decisions or make choices. Hence we leave behind many things which may also be critical in nature. There's a book by Daniel Kahneman titled Thinking, Fast and Slow, which is all about survival, natural instincts and how quickly a human can make decisions. There is another interesting concept that has emerged over the time we call 'satisficing', meaning are there the decisions we make for which we cannot process everything at the same time, and cannot look at every possible scenario. Although businesses have become complex, it is sufficient enough to make those decisions and go ahead. Of course, there is a risk associated, so we do a little bit of risk profiling and management. By taking fast thinking which is spur of the moment and slow thinking which is longer for 'satisficing', it is this combination that's allowing current generation of tech and applications to go deeper into areas of reasoning. This is the broad direction. Technology is moving, a lot of innovation and research is happening across sectors and across industries. That's the primary purpose. A result of that could be bigger, more powerful, more networks with more parameters. Or solutions that go deep and narrow to solve a problem that's very specific, with precision. At that point, you move from large language models to small language models. Q. What's at the core of Tredence's approach to building workplace solutions that meld humans and agentic AI tools, especially as AI becomes more autonomous in decision-making and task execution? SM: I'll take you back a little bit in the journey with something I have always quoted in my interactions. When we were growing up, the skill of stenography was critical. The typewriter came, and that skill evolved. Then document apps came, and that skill evolved further. Of course the advancement was much better for all those things, but these things didn't happen overnight. In the similar context today, when we look at autonomous agents, it is not that tomorrow what we are doing is completely gone because we have not reached that maturity stage. The second thing is a copilot kind of an agent where you start with a human in the loop. Then in the middle is the semi autonomous, where there are certain rules and guardrails being put in and the agent is actually conforming to, working within those boundaries, and doing things with right precision and accuracy. If it is delivering the right kind of goals and outcomes, then there is enough confidence to make it semi autonomous where there is still a human but that human has moved from being integral to the loop, to one that's observing if there are boundary violations or conflicts between human and autonomous agent. These agents and the technology is self learning. It remembers your preferences and analyses decision choices on its own. Many of these nuances that humans take a longer time to train for. The machines will be trained much faster, so there will be a time when the semi autonomous activities will become autonomous. And that is when the human has to wander around. And this is where humans will actually move slightly above the loop because at that point there will be many agents working can call it an agentic mesh, which will be a multi-agent network of architectures and systems operating in a complex enterprise scenario. The human role also needs to evolve from how we look at a copilot today, because tomorrow it will be an integral part of the workforce. Today it may be a team of 10 people with a leader, but tomorrow it will be a leader and maybe five people of varied skills or expertise alongside five agents that are similarly varied. In such situations, a human also has to develop algorithmic empathy because today we are saying 'I don't trust it', but tomorrow you have to start to trust it and you have to start to make decisions based on their inputs. It's a transformational journey and maturity levels will vary. And hence there has to be a mechanism of trust and collaboration. At Tredence, what we are trying to do given this kind of a spectrum and newer complexity of emerging skills and behaviour patterns, is working on various kinds of models and solutions. A question we ask regularly is, what combination of agentic workflow and human expertise can come together to solve this kind of problem? Any solution at the end of the day needs to solve a problem and make an impact from a business outcome perspective. We also are doing persona based solutioning. Think of this as a data analyst solving a very open-ended multi-turn research-hypothesis oriented problem solving which is what every business does, whether for their strategy for growth or new product launches or geographic expansions. There's a lot of algorithmic intervention and a lot of data understanding is required. Q. To build these solutions, are you using in-house models or a mix of third party models such as from Open AI or Anthropic? SM: We have strong alliance relationships, but that doesn't matter. We do a discovery and fit-gap assessment with every client and their environments up front. Our philosophy is that it has to be a composable architecture, meaning the best of an OpenAI model for instance, and the best of an open-source model that needs to come together to solve some of these very complex problems. We keep it transparent, along the way. Q. Which specific roles are being increasingly replaced by Agentic AI implementation? How have the results been thus far? SM: I've been contemplating that, and have come to an understanding that while earlier job roles were defined by either expertise or experience driven and hence there was a hierarchy of things. But today, much of that expertise is actually capsuled into a particular agent. So the learning itself is going in there as an agent and there is a defined input, with a defined output. The job roles and hierarchies are also changing which is gradually changing some of the roles which were earlier about people management. Now you have to become not only a people manager, but also an agent not that we don't need people managers because the human has to be somewhere in the loop. That is one role I see needs upskilling and redesigning. The second role is what I see primarily as about not doing everything because with agentic workflows coming into play, it is about how you can make it more collaborative. The middle manager and technology-driven roles are evolving into how I can look at routine tasks in an autonomous manner and how now I can evolve into a strategic design thinker. I think we have got a better job to do. Critical thinking is important and that is what we're training a lot of our folks for. Today, probably 60% of our time is spent on writing lines of code, but you need to evolve into thinking how this code is going to work, or critical scenarios where it will fail, or running simulations. Q. How important does it become for governance frameworks and regulation to dictate AI compliance, and what would your expectations be from such a set of regulations? SM: There have already been regulations on data privacy and security. It was always focused on data, types and biases, and what you can use a user's data for. Of course that is very foundational, but equally important. Now when you have these algorithms taking over and coming into the mix, there is another side of these regulations that needs to evolve as well. What are the thresholds, what are the boundaries, which application can be used how, which context this can be used in, if those are mission critical cases, and where its AI led with human support or the reverse. All of this represents a very thin line. In many cases also it is about cyber security and the threat management side of it. Those policies need to evolve as well. At the end of the day, all these algorithms and deep learning models are actually data hungry. When a model gets done, it is launched and applications are designed around it. It is doing something, but then data is also changing. So if you have trained these models using a certain set of data from maybe five year back, but now it is a different scenario and hence refreshing the model is important. The currency of the data, it is those kinds of regulations that need to happen, particularly for pharmaceutical companies and healthcare or banking or life sciences, where you cannot have old or stale data.
The Hindu
28-07-2025
- Business
- The Hindu
Fulcrum Digital to hire 100 more employees for Coimbatore centre
At a time when large Information Technology (IT) companies are gearing up to reduce workforce due to most coding job now being done by Artificial Intelligence (AI) agents and on account of stress in the global economy, Fulcrum Digital, a sub $100 million revenue U.S.-based company has decided to expand its Coimbatore campus by hiring over 100 more people by next year. The company which has deployed hundreds of AI agents across campuses said it was continuing to hire talent with AI agents working side by side with humans to enhance efficiency and ensure quicker delivery to clients. 'We continue on hiring employees. We have not reduced number of employees. However, as programming has improved using AI, 25% to 30% of our programming is now getting enhanced by AI agent. So AI agents are now coding for us. And humans are going to be coexisting with them,' said Rajesh Sinha, Founder & Chairman, Fulcrum Digital in an interview. 'We are maturing on our AI coding journey for our own development work. And we are measuring the percentage of adoption inside the company. That has certainly given a growth because it has given the speed and more value creation in less time. We have also added human workforce to fulfill the demand of our customers,' he added. He said in the company's South India Development Center at Coimbatore the existing employee strength of 50 people team will grow up to 150 people by end of next year. 'We are envisioning in next two years to be 3,000 employees [organization] and I am hoping that from 150 we should go to about 300 to 400 people in Coimbatore. And at that stage we will look for our own campus or building which we can house people there,' he added. 'Apart from people, we also want to add more AI agents. So, I always say that while we become 1,800 people this year, we will have 300, 400 AI agents coexisting with our team members,' he said. He said apart from accelerating the coding, the company would have more AI agents doing the development work. 'Similar approach is happening in Coimbatore. There we will have about 500 people and I am expecting 100 AI agents along with the 500 people doing the work for us,' he added. The company has budgeted about $7 to $8 million for the Coimbatore development centre. According to experts, AI has already started driving charges across business organisations. Munjay Singh, Chief Operating Officer, Tredence, a data science solutions provider said, 'Generative and Agentic AI are no longer experimental; they are driving real change across companies.' 'We are witnessing a clear shift from pilots to controlled productionization, particularly in areas such as marketing operations, supply chain, and sales operations. Agentic workflows are evolving from simple automation to semi-autonomous systems that enhance teams rather than replacing them,' he said. He said by bringing intelligence to the task level, AI tools were making processes self-sufficient and smarter. 'This transformation is being guided by robust governance, human-in-the-loop oversight, and a deep focus on responsible scaling,' he said. 'It's not about cost-cutting—it's about building capability. As AI matures, it's giving rise to hybrid talent models and collaborative ecosystems where humans and intelligent agents thrive together,' he added.
Yahoo
09-07-2025
- Business
- Yahoo
Tredence Launches Agentic AI Playbook for CDAOs to Scale Enterprise Modernization
Designed for enterprises moving beyond experimentation, the playbook challenges outdated leadership models in the GenAI age SAN JOSE, Calif. and BENGALURU, India, July 9, 2025 /PRNewswire/ -- Tredence today announced the launch of the Agentic AI Playbook, a first-of-its-kind strategic guide for CDAOs and AI leaders navigating the transition from AI pilots to enterprise-scale modernization. The playbook offers a bold, practical framework for reimagining workflows, decision-making structures, and leadership models in the GenAI era. As enterprises race to deploy AI across business functions, the Agentic AI Playbook challenges the prevailing focus on tools and models. Instead, it urges leaders to address the fundamental question: How must organizations evolve when AI agents become central to decision-making and execution? Unlike typical AI reports focused on tools and trends, the playbook offers a contrarian perspective: the biggest risk with AI is not misuse, it's underuse due to outdated organizational design. The playbook positions AI not as a bolt-on solution but as a force reshaping workflows, decision rights, and business models. "Many Agentic AI discussions today are still tactical—focused on use cases, models, and tools. But leaders don't scale strategy through pilots," said Sumit Mehra, Co-founder and CTO of Tredence. "This playbook is built for those designing organizations where humans and machines are peers in decision-making. That shift requires new mental models, not just new tech." The Agentic AI Vision Playbook is anchored in five strategic lenses: Business Value Realization: Structuring AI initiatives to deliver measurable ROI, sustain stakeholder engagement, and maximize long-term value. Human + AI Agents = Co-Intelligence: Redefining the role of humans in an AI-automated world and ensuring alignment between human strategy and machine execution. Business Process Reengineering: Using decision intelligence and Agentic AI systems to automate and optimize end-to-end workflows. Technology Evolution: Adapting to emerging AI innovations such as quantum computing, brain-computer interfaces, and small, domain-specific AI models. Governance & Compliance: Creating agile compliance frameworks that embed responsible AI principles, integrate new regulations, and scale AI adoption across organizations and ecosystems. Each lens is mapped across three phases of maturity: Now – What leaders must act on in the next 12 months New – How operating models and systems evolve in 2 to 3 years Next – What long-term leadership looks like in AI-native organizations The playbook distills insights from Tredence's cross-industry work with Fortune 500 clients and was co-developed with perspective from executives at Mars, Nestlé, Casey's, Databricks, Google Cloud, Snowflake, Forrester, IDC among the others. The playbook provides strategies to embed AI agents across enterprises—streamlining supply chains, strengthening data governance, and transforming customer experiences through real-time insights and automation. "We've seen AI pilots fail not due to technology, but because organizations weren't ready—lacking clear decision structures, governance, and accountability for human-machine collaboration," said Soumendra Mohanty, Chief Strategy Officer at Tredence. "As AI agents take on more decisions, leaders must rethink when humans stay in, oversee, or step back from the loop. This playbook guides leaders to build the right systems, teams, and mindsets to scale GenAI successfully." The full playbook is available for download at About Tredence Tredence is a global data science and AI solutions provider focused on solving the last-mile problem in AI – the gap between insight creation and value realization. Tredence leverages deep domain expertise, data platforms and accelerators, and strategic partnerships to provide targeted, impactful solutions to its clients. The company has 3,500+ employees across San Francisco Bay Area, Chicago, London, Toronto, and Bengaluru, serving top brands in Retail, CPG, Hi-tech, Telecom, Healthcare, Travel, and Industrials. For more information, visit and follow us on LinkedIn. Video: View original content to download multimedia: SOURCE Tredence Sign in to access your portfolio
Business Standard
09-07-2025
- Business
- Business Standard
Tredence Launches Agentic AI Playbook for CDAOs to Scale Enterprise Modernization
PRNewswire San Jose (California) [US] / Bengaluru (Karnataka) [India], July 9: Tredence today announced the launch of the Agentic AI Playbook, a first-of-its-kind strategic guide for CDAOs and AI leaders navigating the transition from AI pilots to enterprise-scale modernization. The playbook offers a bold, practical framework for reimagining workflows, decision-making structures, and leadership models in the GenAI era. * Designed for enterprises moving beyond experimentation, the playbook challenges outdated leadership models in the GenAI age As enterprises race to deploy AI across business functions, the Agentic AI Playbook challenges the prevailing focus on tools and models. Instead, it urges leaders to address the fundamental question: How must organizations evolve when AI agents become central to decision-making and execution? Unlike typical AI reports focused on tools and trends, the playbook offers a contrarian perspective: the biggest risk with AI is not misuse, it's underuse due to outdated organizational design. The playbook positions AI not as a bolt-on solution but as a force reshaping workflows, decision rights, and business models. "Many Agentic AI discussions today are still tactical--focused on use cases, models, and tools. But leaders don't scale strategy through pilots," said Sumit Mehra, Co-founder and CTO of Tredence. "This playbook is built for those designing organizations where humans and machines are peers in decision-making. That shift requires new mental models, not just new tech." The Agentic AI Vision Playbook is anchored in five strategic lenses: 1. Business Value Realization: Structuring AI initiatives to deliver measurable ROI, sustain stakeholder engagement, and maximize long-term value. 2. Human + AI Agents = Co-Intelligence: Redefining the role of humans in an AI-automated world and ensuring alignment between human strategy and machine execution. 3. Business Process Reengineering: Using decision intelligence and Agentic AI systems to automate and optimize end-to-end workflows. 4. Technology Evolution: Adapting to emerging AI innovations such as quantum computing, brain-computer interfaces, and small, domain-specific AI models. 5. Governance & Compliance: Creating agile compliance frameworks that embed responsible AI principles, integrate new regulations, and scale AI adoption across organizations and ecosystems. Each lens is mapped across three phases of maturity: * Now - What leaders must act on in the next 12 months * New - How operating models and systems evolve in 2 to 3 years * Next - What long-term leadership looks like in AI-native organizations The playbook distills insights from Tredence's cross-industry work with Fortune 500 clients and was co-developed with perspective from executives at Mars, Nestle, Casey's, Databricks, Google Cloud, Snowflake, Forrester, IDC among the others. The playbook provides strategies to embed AI agents across enterprises--streamlining supply chains, strengthening data governance, and transforming customer experiences through real-time insights and automation. "We've seen AI pilots fail not due to technology, but because organizations weren't ready--lacking clear decision structures, governance, and accountability for human-machine collaboration," said Soumendra Mohanty, Chief Strategy Officer at Tredence. "As AI agents take on more decisions, leaders must rethink when humans stay in, oversee, or step back from the loop. This playbook guides leaders to build the right systems, teams, and mindsets to scale GenAI successfully." The full playbook is available for download at About Tredence Tredence is a global data science and AI solutions provider focused on solving the last-mile problem in AI - the gap between insight creation and value realization. Tredence leverages deep domain expertise, data platforms and accelerators, and strategic partnerships to provide targeted, impactful solutions to its clients. The company has 3,500+ employees across San Francisco Bay Area, Chicago, London, Toronto, and Bengaluru, serving top brands in Retail, CPG, Hi-tech, Telecom, Healthcare, Travel, and Industrials. For more information, visit and follow us on LinkedIn.
Malaysian Reserve
09-07-2025
- Business
- Malaysian Reserve
Tredence Launches Agentic AI Playbook for CDAOs to Scale Enterprise Modernization
Designed for enterprises moving beyond experimentation, the playbook challenges outdated leadership models in the GenAI age SAN JOSE, Calif. and BENGALURU, India, July 9, 2025 /PRNewswire/ — Tredence today announced the launch of the Agentic AI Playbook, a first-of-its-kind strategic guide for CDAOs and AI leaders navigating the transition from AI pilots to enterprise-scale modernization. The playbook offers a bold, practical framework for reimagining workflows, decision-making structures, and leadership models in the GenAI era. As enterprises race to deploy AI across business functions, the Agentic AI Playbook challenges the prevailing focus on tools and models. Instead, it urges leaders to address the fundamental question: How must organizations evolve when AI agents become central to decision-making and execution? Unlike typical AI reports focused on tools and trends, the playbook offers a contrarian perspective: the biggest risk with AI is not misuse, it's underuse due to outdated organizational design. The playbook positions AI not as a bolt-on solution but as a force reshaping workflows, decision rights, and business models. 'Many Agentic AI discussions today are still tactical—focused on use cases, models, and tools. But leaders don't scale strategy through pilots,' said Sumit Mehra, Co-founder and CTO of Tredence. 'This playbook is built for those designing organizations where humans and machines are peers in decision-making. That shift requires new mental models, not just new tech.' The Agentic AI Vision Playbook is anchored in five strategic lenses: Business Value Realization: Structuring AI initiatives to deliver measurable ROI, sustain stakeholder engagement, and maximize long-term value. Human + AI Agents = Co-Intelligence: Redefining the role of humans in an AI-automated world and ensuring alignment between human strategy and machine execution. Business Process Reengineering: Using decision intelligence and Agentic AI systems to automate and optimize end-to-end workflows. Technology Evolution: Adapting to emerging AI innovations such as quantum computing, brain-computer interfaces, and small, domain-specific AI models. Governance & Compliance: Creating agile compliance frameworks that embed responsible AI principles, integrate new regulations, and scale AI adoption across organizations and ecosystems. Each lens is mapped across three phases of maturity: Now – What leaders must act on in the next 12 months New – How operating models and systems evolve in 2 to 3 years Next – What long-term leadership looks like in AI-native organizations The playbook distills insights from Tredence's cross-industry work with Fortune 500 clients and was co-developed with perspective from executives at Mars, Nestlé, Casey's, Databricks, Google Cloud, Snowflake, Forrester, IDC among the others. The playbook provides strategies to embed AI agents across enterprises—streamlining supply chains, strengthening data governance, and transforming customer experiences through real-time insights and automation. 'We've seen AI pilots fail not due to technology, but because organizations weren't ready—lacking clear decision structures, governance, and accountability for human-machine collaboration,' said Soumendra Mohanty, Chief Strategy Officer at Tredence. 'As AI agents take on more decisions, leaders must rethink when humans stay in, oversee, or step back from the loop. This playbook guides leaders to build the right systems, teams, and mindsets to scale GenAI successfully.' The full playbook is available for download at About Tredence Tredence is a global data science and AI solutions provider focused on solving the last-mile problem in AI – the gap between insight creation and value realization. Tredence leverages deep domain expertise, data platforms and accelerators, and strategic partnerships to provide targeted, impactful solutions to its clients. The company has 3,500+ employees across San Francisco Bay Area, Chicago, London, Toronto, and Bengaluru, serving top brands in Retail, CPG, Hi-tech, Telecom, Healthcare, Travel, and Industrials. For more information, visit and follow us on LinkedIn. Video: View original content:
