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The Rise of Agentic AI: Market Surges, Enterprise Adoption, and Security Implications

General Report June 1, 2025
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TABLE OF CONTENTS

  1. Emergence and Capabilities of Agentic AI
  2. Market Growth Projections across Sectors
  3. Enterprise Adoption and Solution Deployments
  4. Cybersecurity Transformations and Risks
  5. Workforce Dynamics and Skill Demand
  6. Emerging Trends and Future Outlook
  7. Conclusion

1. Summary

  • As of June 1, 2025, the emergence of Agentic AI—autonomous software agents capable of independent reasoning and action—has marked a significant shift within enterprise operations across various sectors. This technological evolution has transitioned from research prototypes to widespread deployment, reflecting an urgent demand for innovative solutions that streamline workflows and enhance operational efficiency. Recent surveys indicate that approximately 27% of enterprises have successfully integrated AI agents at a production scale, while an additional 31% are exploring proofs of concept. Particularly noteworthy is the leading role of major industry players such as Infosys and Tata, which have launched hundreds of solutions incorporating Agentic AI capabilities. The potential market growth for these technologies is immense, with predictions estimating an astonishing expansion from approximately USD 372 billion in 2025 to over USD 2.4 trillion by 2032. Key sectors such as finance, logistics, and animation are expected to be significant contributors, thriving as they innovate and adapt to AI-driven methodologies. However, alongside these advancements, security experts caution against an escalating 'AI-versus-AI arms race, ' necessitating new frameworks for cybersecurity that evolve at the pace of innovation. The anticipated demand for AI professionals is projected to soar, particularly in India where an estimated one million AI-related roles are forecasted by 2026, illustrating a firm commitment to upskilling a workforce prepared for a futuristic job market.

  • Consequently, both the qualitative transformation in organizational structures and the quantitative growth in market value underscore the necessity for enterprises to adopt strategies that embrace Agentic AI. The landscape is not merely marked by technological adoption; it is defined by the need for responsible and sustainable frameworks to manage the proliferation of these intelligent systems. Companies are increasingly prioritizing investments in areas that necessitate skilled expertise, reflecting a holistic view of AI integration—from automation and process management to enhanced customer engagement in a highly competitive digital economy.

2. Emergence and Capabilities of Agentic AI

  • 2-1. Evolution of Agentic AI from experimentation to deployment

  • The evolution of Agentic AI marks a significant shift in how artificial intelligence is deployed within enterprises. Initially, AI was predominantly utilized for single-purpose applications, performing specific tasks with limited autonomy. However, as businesses increasingly demand solutions that can not only execute predefined actions but also adapt and learn in real time, Agentic AI has emerged as a critical advancement. According to a recent report by Nasscom, approximately 27% of enterprises reported having AI agents in production or at scale, while another 31% were at the proof-of-concept stage. Furthermore, 30% of enterprises planned to initiate proofs of concept or scale their deployments within the current year.

  • Major firms such as Infosys have launched over 200 AI agents as part of their Topaz AI offerings, reflecting a strategic move towards deploying technology that can autonomously manage intricate workflows across various sectors including finance, healthcare, and manufacturing. The company's initiatives illustrate a broader pattern of transformation as enterprises pivot from experimenting with AI solutions to implementing them within operational frameworks aimed at enhancing efficiency and productivity. Infinitely more capable than their predecessors, these AI agents are designed to autonomously navigate complex tasks, from predictive network capacity planning to inventory management, ultimately enabling a shift towards synchronous AI architectures.

  • As noted in industry commentary, the future of enterprise transformation hinges on AI agents, which unlock new levels of productivity and facilitate human-machine collaboration. Evidence points to enterprises adapting their budgets to reflect this significant shift, as they increasingly prioritize investments in autonomous, agent-based systems over traditional AI applications.

  • 2-2. Core capabilities and use cases of autonomous AI agents

  • The core capabilities of Agentic AI agents extend beyond traditional functionalities associated with artificial intelligence. These agents are characterized by their ability to independently process information, make decisions, and take actions without continuous human oversight. This fundamental shift towards autonomy is reshaping operational dynamics across many sectors. For instance, in the manufacturing industry, AI agents enable predictive maintenance by anticipating equipment failures, considerably reducing downtimes and operational costs. An automotive manufacturer achieved a 35% reduction in quality defects and a 22% boost in production throughput through the integration of autonomous AI systems across smart factory environments.

  • In the financial sector, agentic AI facilitates enhanced fraud detection by analyzing complex patterns and identifying anomalies with greater accuracy. AI agents streamline KYC processes and client onboarding, cutting down processing times dramatically while improving compliance rates. A notable success was reported by a leading global bank that reduced its KYC processing time from three days to four hours, demonstrating the potential for improved operational efficiency through intelligent automation.

  • Healthcare and life sciences fields also benefit from the capabilities of Agentic AI. These systems assist professionals in analyzing vast patient data sets, accelerating diagnosis, and delivering better clinical support. For example, Mayo Clinic implemented AI agents for care coordination, leading to a 28% decrease in patient readmission rates.

  • With their flexibility and adaptability, agentic AI agents address complex problems across diverse environments. The capacity for continuous learning plays a crucial role in their ability to evolve and respond to changing business conditions. Organizations that successfully adopt these technologies can experience significant productivity gains—on average, implementations have led to a reported productivity increase of 37% in targeted workflows, as per Forrester research.

3. Market Growth Projections across Sectors

  • 3-1. Overall AI market forecast to 2032/2034

  • The global Artificial Intelligence (AI) market is on a robust growth trajectory, predicted to reach approximately USD 2.4 trillion by 2032, reflecting a compound annual growth rate (CAGR) of around 30.6% from an estimated USD 372 billion in 2025. Major players such as Microsoft, IBM, and Google are pivotal in this expansion, particularly through advancements in autonomous AI technologies and generative models (Cited from MarketsandMarkets™ report, May 2025).

  • More ambitiously, some reports suggest that by 2034, AI market value may skyrocket to USD 3.68 trillion, underlining the increasing integration of AI solutions across various sectors, particularly in finance and logistics (Cited from Precedence Research report, February 2025). The shift towards AI-native infrastructure and real-time processing capabilities further supports this rapid growth.

  • 3-2. Growth of Generative AI in Financial Services

  • Generative AI is set to revolutionize the financial services sector, with the global market projected to expand from USD 2.7 billion in 2024 to approximately USD 18.9 billion by 2030, reflecting a CAGR of 38.7% during this period. This growth is driven by the increasing complexity of financial markets and a rising demand for personalized financial solutions, such as customized investment strategies, powered by AI techniques for data analysis and risk assessment (Cited from Generative Artificial Intelligence in Financial Services Strategic Business Report 2025, March 2025).

  • Additionally, the market is expected to benefit from the ongoing integration of generative AI with blockchain technology. This integration promises enhanced transaction security and operational efficiency—qualities vital for contemporary financial institutions to remain competitive.

  • 3-3. Expansion of AI in Animation and Logistics

  • The market for Generative Artificial Intelligence in Animation is projected to grow dramatically, 预计 reach USD 15.9 billion by 2030, up from USD 2.1 billion in 2024. This translates to an impressive CAGR of 39.8%, propelled by advancements in AI algorithms and an increasing demand for high-quality, visually captivating content that meets the demands of a content-driven digital economy. Generative AI tools are currently enabling creators to streamline production processes and enhance creativity (Cited from GenAI in Animation Global Business Analysis Report 2025, May 2025).

  • In the logistics sector, the Generative AI market is anticipated to grow from USD 1.3 billion in 2024 to USD 7.0 billion by 2030, at a CAGR of 32.5%. This growth is driven by the integration of AI with IoT technologies to improve supply chain efficiency and the rising need for automation in response to the surge in e-commerce activities. The ability of generative AI systems to analyze real-time data will become crucial in optimizing inventory management and logistics operations, offering businesses the agility needed in an increasingly competitive landscape (Cited from Generative Artificial Intelligence in Logistics Business Intelligence Report 2025, May 2025).

  • 3-4. Predictive Analytics market outlook

  • The outlook for the market of predictive analytics is exceedingly positive, with many reports indicating that growth will be driven by the escalating complexity of data environments and a desire for organizations to derive actionable insights quickly. As businesses across various sectors increasingly rely on data-driven decision-making processes, the demand for predictive analytics solutions is expected to multiply. Improvements in machine learning technologies are facilitating the creation of more accurate predictive models, enabling companies to forecast trends, optimize operations, and enhance customer engagement effectively (Cited from emerging trends observed in AI technology reports).

  • Forecasts suggest that the predictive analytics market will reflect similar growth patterns as seen in overarching AI trends, potentially reaching multi-billion-dollar valuations by 2030. Organizations are recognizing the value of investing in AI-powered analytics, which promises increased operational efficiency and improved risk management across sectors like healthcare, finance, and logistics.

4. Enterprise Adoption and Solution Deployments

  • 4-1. Infosys Agentic AI Foundry and Topaz™ offerings

  • On May 29, 2025, Infosys announced the launch of the Infosys Agentic AI Foundry, a transformative solution aimed at accelerating the deployment of AI agents within enterprise settings. This comprehensive platform, which is part of the Infosys Topaz suite, is designed to provide organizations with a strategic roadmap to responsibly and effectively adopt AI technologies. The Foundry facilitates the integration of AI agents across various business functions, improving decision-making, enhancing customer experience, and increasing operational efficiency. Its open architecture promotes flexibility, ensuring that businesses are not locked into a single technology provider, thus promoting sustainability and growth potential in their AI initiatives.

  • Infosys emphasized the ability of the Agentic AI Foundry to incorporate reusable components, including both pre-built and customizable AI agents that can be deployed across different business operations. The solution has already demonstrated substantial effectiveness: for instance, it helped a technology firm reduce support resolution times by 50%, leading to a significant 24% increase in customer satisfaction. Additionally, the deployment within Infosys' own finance department of a multi-agent invoice automation solution exemplified its capacity to improve productivity by over 50%, thereby delivering considerable cost savings.

  • 4-2. Infosys BPM’s AI agents for finance and accounting

  • In a notable development for financial services, Infosys BPM unveiled its AI agents specifically tailored for invoice processing, which are part of its flagship Accounts Payable on Cloud solution, announced on May 30, 2025. This innovation represents a shift from traditional, human-driven processes to an autonomous AI-first approach, promising heightened efficiency and accuracy in financial operations.

  • The AI agents are adept at managing end-to-end workflow processes with minimal human oversight. This advancement allows businesses to adapt to dynamic operational demands swiftly. For example, Americana Restaurants, the largest restaurant operator in the Middle East and North Africa, has adopted this AI-powered solution. They reported improvements in invoice processing speed and accuracy, illustrating the substantial impact of these technologies on operational agility in the finance domain.

  • 4-3. Tata and other IT firms deploying agentic solutions

  • Tata Consultancy Services (TCS), among other IT firms, has also made remarkable strides in adopting agentic AI technologies. With the commitment to enhance human-machine collaboration, TCS's approach aligns with the broader industry trend of integrating AI agents into various operational processes. As reported in recent communications, TCS is focusing on establishing a large pool of AI agents that work alongside human employees to solve complex business challenges, evolving its offerings from singular-purpose AI solutions to more sophisticated, autonomous frameworks capable of significant decision-making and operational insights.

  • The move reflects a trend observed across industries, where organizations are increasingly utilizing agentic AI to drive transformation and efficiencies in sectors such as healthcare, finance, telecom, and manufacturing. As of late May 2025, a significant portion of enterprises (27%) have reported that they are already deploying agentic AI at scale, while many others are progressing through proofs-of-concept. This indicates a robust momentum towards the widespread operational embrace of AI-driven solutions.

5. Cybersecurity Transformations and Risks

  • 5-1. The AI-versus-AI arms race in threat detection

  • The dynamics of cybersecurity are transforming dramatically with the introduction of Agentic AI, which is capable of autonomous functions such as decision-making and threat detection. As reported during the RSA Conference 2025, discussions increasingly centered on the 'AI-versus-AI arms race, ' where both cyber attackers and defenders utilize sophisticated artificial intelligence tools. This dual use of technology raises the stakes significantly—while organizations attempt to bolster their defenses with AI, adversaries are simultaneously employing similar technologies to execute more sophisticated and adaptive cyber attacks. The capability of AI agents to analyze vast amounts of data and learn from each encounter allows them to not only detect threats in real-time but also react more swiftly than traditional systems. Hence, a continuous cycle of evolution is underway, where any lag in response can result in disastrous breaches or organizational vulnerabilities.

  • Recent insights from cybersecurity experts indicate that as AI frameworks for threat detection become more advanced, they inevitably enhance the potential for adversaries to automate their attacks as well. The Food for Thought report outlined that 78% of Chief Information Security Officers (CISOs) have identified a rise in AI-based threats in recent quarters. This escalation demands a paradigm shift in how organizations perceive and address cybersecurity challenges. Traditional response strategies, which have typically been reactive, need to evolve into a more proactive approach, where the dual nature of AI implementation is understood and addressed comprehensively.

  • To effectively navigate this era of uncertainty, organizations are now focusing on creating a symbiotic relationship between autonomous AI systems and human expertise, where human analysts collaborate with AI systems to interpret situations better and respond more efficiently.

  • 5-2. Weaponization and harm scenarios of agentic AI

  • The introduction of agentic AI not only provides opportunities for advancement but also raises serious concerns regarding its potential weaponization. As highlighted by Steve Durbin, Chief Executive of the Information Security Forum, the danger lies in agentic AI's capacity to automate malicious activities, including sophisticated malware attacks, automated social engineering scams, and complex phishing campaigns. For instance, the ability of agentic AI to form polymorphic malware creates a scenario where harmful software can alter its code to evade detection by security measures.

  • Furthermore, agentic AI can proactively discover vulnerabilities within networks, identify weak points, and even facilitate synthetic identity fraud, where adversaries create believable personas to execute fraudulent activities. The AI-driven capabilities severely complicate the landscape for defenders, as hackers become increasingly reliant on these automated systems to orchestrate their attacks at a scale and speed previously unattainable.

  • Given its potential for harm, a conversation around how organizations can safeguard against these threats must take place. Mitigation strategies that incorporate user access control, data protection mechanisms, and anomaly detection powered by AI itself are essential to counteract the risks presented by weaponized AI.

  • 5-3. Proactive and adaptive AI agents in defense

  • In contrast to the threats posed by adversaries, the positive implications of agentic AI are also significant, particularly in bolstering cybersecurity defenses. As identified by cybersecurity leaders, AI has transitioned from being merely reactive to becoming proactive. Agentic AI systems are now capable not only of identifying threats as they emerge but also of predicting potential vulnerabilities based on historical patterns. A proactive security approach enables organizations to anticipate and mitigate risks before they materialize.

  • For example, during a recent incident of cloud-based brute-force attacks, an Agentic AI system demonstrated its utility by identifying anomalous login attempts in real-time and autonomously engaging defensive measures such as blocking suspicious traffic instantaneously. This contrasts sharply with traditional systems that might alert human operators only after sufficient evidence is gathered, wasting valuable time that could allow attackers to exploit their targets.

  • Moreover, as these systems evolve, they are increasingly able to adapt their defenses based on ongoing threats, learning continuously from new data inputs to enhance resilience over time. This adaptability is crucial in an environment where cyber threats are not static but constantly evolving. By investing in proactive agentic AI solutions, organizations can facilitate a more robust and responsive cybersecurity posture.

  • 5-4. Key insights from RSA Conference 2025

  • The RSA Conference 2025 highlighted pivotal trends and insights regarding the future of cybersecurity, particularly in relation to the integration of AI technologies. A central theme was the clear transition from generative AI to agentic AI, with notable developments showcasing how both industries and security vendors are embracing this shift. Companies unveiled several advanced technologies designed to automate various cybersecurity functions, streamlining operations and enhancing threat response capabilities.

  • Furthermore, discussions emphasized the need for clearer governance around AI implementations to mitigate risks associated with autonomous systems. Many experts underscored that although AI can dramatically enhance the efficiency of Security Operations Centers (SOCs), human oversight remains essential, especially in contexts requiring the nuanced understanding that only human analysts can provide. These insights from RSA indicate a crucial turning point as firms recognize the immense potential of agentic AI while acknowledging the complexities that come with its deployment.

6. Workforce Dynamics and Skill Demand

  • 6-1. Projected demand for AI professionals in India

  • As of June 1, 2025, the projected demand for Artificial Intelligence (AI) professionals in India is on track to reach one million by 2026. This significant increase is driven by various factors including India's ambition to transform into a USD 23-35 trillion economy by 2047 and the ensuing demand for a highly skilled workforce capable of navigating the complexities of AI and automation. Future educational initiatives are increasingly being aligned with this trend, as institutions enhance their engineering programs to incorporate interdisciplinary learning that bridges AI, machine learning, cybersecurity, and other tech domains. The All-India Council for Technical Education (AICTE) reports a notable 16% rise in approved B. Tech seats over the past four years, particularly emphasizing the growth of programs in Computer Science and related fields, indicating a robust pipeline of talent designed to fill this anticipated demand. The push for specialized AI programs reflects both the immediate requirements of the job market and long-term strategic goals, highlighting a transformation in educational frameworks to better prepare students for forthcoming roles in AI.

  • 6-2. Impact of AI on entry-level tech job displacement

  • The landscape of employment within the tech sector is undergoing a profound shift, particularly affecting entry-level positions. As reported on May 31, 2025, hiring among recent graduates has diminished, with a 25% decrease in new tech grad hires at major firms and an 11% drop at startups for 2024 compared to the previous year. This trend highlights a concerning displacement of entry-level roles due to the rising adoption of AI tools that automate tasks previously performed by human workers. According to a World Economic Forum survey, 40% of employers foresee replacing human roles with AI where applicable. In this evolving environment, companies are reallocating budgets towards more experienced hires in specialized areas like AI infrastructure and advanced data operations, which are perceived to deliver greater efficiency and return on investment. As a result, the traditional pathway for new graduates entering the tech field is being altered, leading to questions about the sustainability of entry-level positions in this rapidly changing market. The adaptation to these shifts will be crucial for graduates seeking to find their footing in the newly defined job landscape.

7. Emerging Trends and Future Outlook

  • 7-1. Finance leaders’ plans for agentic AI adoption

  • A recent survey conducted by Wolters Kluwer reveals a significant shift in the financial sector, where only 6% of finance leaders currently utilize agentic AI technology. However, an ambitious 38% plan to adopt these systems within the next year, potentially increasing the proportion of finance teams employing agentic AI to 44% by 2026. This anticipated surge reflects the industry's urgent quest for efficiency and resilience amid ongoing economic volatility. The survey underscores the growing recognition that agentic AI—systems capable of executing tasks autonomously—holds substantial promise for enhancing operational efficiency and transforming financial planning and analysis (FP&A) processes. Moreover, this shift is accompanied by a need for new skills within finance teams, as 85% of respondents categorize AI proficiency as vital for recruitment purposes, indicating a shift towards an AI-augmented workforce in finance.

  • 7-2. Role of AI in startup innovation and leadership

  • The landscape of AI innovation is rapidly evolving, particularly within the startup ecosystem. As delineated in a comprehensive assessment by Morgan Stanley, key trends influencing AI development in 2025 include the integration of reasoning-capable models and generative AI tools that enhance productivity. This trend is being propelled by startups focusing on vertical solutions tailored to industry-specific needs, leading to increased operational efficiencies and innovation. Startups are leveraging agentic AI to create more autonomous systems capable of complex decision-making, thus moving beyond traditional automation. Such advancements are not only driving productivity but are also reshaping leadership dynamics, as executives must navigate and adapt to a landscape where AI serves as a collaborator rather than a mere tool.

  • 7-3. Projected 327% surge in agent deployments by 2027

  • The proliferation of agentic AI is underscored by projections indicating a staggering 327% increase in AI agent deployments by 2027. A report from Medium highlights that the deployment of AI agents across various sectors—ranging from customer engagement to complex supply chain management—is set to redefine enterprise automation standards. As these agents evolve from traditional rule-based systems into sophisticated, adaptive collaborators, organizations will need to reassess their digital strategies. The expected increase in agentic AI adoption implies that companies must integrate multi-agent systems capable of performing complex workflows autonomously, which will fundamentally transform operational frameworks and decision-making processes across industries.

  • 7-4. Tech executives driving autonomous AI integration

  • According to a survey by Ernst & Young, technology leaders envision a future where over half of their AI systems will operate autonomously within two years. This marks a significant transition from exploratory pilot programs to substantial implementations of agentic AI within enterprise operations. As tech executives embrace this shift, they recognize that autonomous systems can enhance efficiency, agility, and innovation in unprecedented ways. Moreover, with around 48% of surveyed executives already in the process of integrating AI agents, the momentum behind employee-reskilling initiatives is palpable. CHROs (Chief Human Resource Officers) are taking proactive steps to realign roles and redefine skill requirements for a future-oriented workforce, ensuring that organizations remain competitive amid the rapid evolution of AI technologies.

Conclusion

  • The transition of Agentic AI from experimental stages to strategic implementation marks a critical juncture in the evolution of technologies poised to redefine enterprise capabilities. By June 1, 2025, it is evident that the trajectory of AI adoption is not simply a trend but a foundational shift that is forecast to yield multitrillion-dollar markets by the early 2030s. The acceleration in sector-specific applications—especially in finance, logistics, and media—not only demonstrates the versatility of Agentic AI but also highlights the urgency for enterprises to develop robust governance frameworks, cultivate specialized talent, and enhance cybersecurity measures to safeguard against emerging threats. The integration of AI-driven solutions across various organizational functions is not merely about operational efficiency; it is about reimagining the workplace and establishing collaborative environments where human and machine intelligence coexist and bolster one another.

  • Looking forward, the convergence of Agentic AI with cutting-edge technologies such as edge computing, custom silicon, and domain-specific models sets the stage for the next wave of digital transformation. As organizations adapt to these realities, the emphasis on responsible innovation and agile adaptability will serve as pivotal competitive advantages in an era characterized by rapid technological evolution. Stakeholders within industries must engage in continuous collaboration to design industry standards and practices that not only mitigate misuse of technologies but also enable an environment that fosters creativity and encourages responsible deployment of AI applications. As we navigate this transformative landscape, the outcome will hinge on how effectively organizations embrace these changes while prioritizing ethical considerations and workforce development.

Glossary

  • Agentic AI: Agentic AI refers to autonomous software agents that have the capability to reason, make independent decisions, and execute actions without needing continuous human oversight. This technology has transitioned from experimental phases to deployment in various sectors, transforming operations and enhancing productivity.
  • Autonomous Agents: Autonomous agents are computer programs that perform tasks or functions on their own, adapting and learning from their environment. In the context of Agentic AI, these agents can handle complex workflows and make decisions that traditionally required human intervention, significantly affecting operational efficiencies.
  • Cybersecurity: Cybersecurity encompasses measures and protocols implemented to protect systems, networks, and data from unauthorized access or attacks. As Agentic AI becomes integrated into this field, it presents both advanced defensive capabilities and new threats, necessitating a reevaluation of security strategies.
  • Infosys: Infosys is a multinational corporation based in India that provides IT services, including the development of Agentic AI solutions. As of June 2025, Infosys has launched various offerings aimed at integrating AI into enterprise environments, enhancing operational efficiency and decision-making.
  • Predictive Analytics: Predictive analytics involves using statistical techniques and algorithms to analyze current and historical data to make forecasts about future events. Growing demand for predictive analytics solutions is expected as organizations prioritize data-driven decision-making to improve operational performance.
  • Generative AI: Generative AI refers to algorithms that can create new content or data that resembles an existing set of data. It is increasingly intertwined with applications in various sectors such as financial services and animation, significantly impacting creativity and production processes.
  • Market Growth Projections: Market growth projections are forecasts that predict the expansion and revenue generation potential of specific industries or sectors over time. As of June 2025, AI markets are projected to grow significantly, with estimates suggesting values rising to USD 2.4 trillion by 2032.
  • AI-versus-AI Arms Race: The AI-versus-AI arms race refers to the escalating scenario where both cybersecurity defenders and attackers leverage sophisticated AI technologies in their strategies. As organizations enhance their AI defenses, adversaries are adopting similar tools to conduct more complex and adaptive cyberattacks.
  • Enterprise Adoption: Enterprise adoption refers to the integration of new technologies and methodologies within corporate structures to streamline operations and enhance productivity. The current trend indicates a significant rise in the use of Agentic AI solutions across various industries.
  • Workforce Demand for AI Professionals: The workforce demand for AI professionals signifies the increasing need for skilled individuals capable of developing and managing AI technologies. In India, this demand is expected to hit one million positions by 2026, emphasizing the importance of education and training in the AI sector.

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