We are introducing DeepSeek-R1 in two blog posts that have recently surprised us. In this Part 2, we discuss the paradigm shift brought by DeepSeek-R1. Previously, in Part 1, we covered the background of its emergence and detailed the technical aspects of the model. Although the posts are long, we recommend reading both for a complete understanding.
Part 2 of the DeepSeek-R1 series explores the shift from pre-training-focused AI to inference-driven AI and the rise of Agent AI. As scaling limitations become evident, models like DeepSeek-R1 prioritize autonomous reasoning and task execution, paving the way for AI systems that plan, interact, and optimize independently. This transformation is expected to redefine industries by enhancing automation, decision-making, and operational efficiency.
This shift signals a new era of AI where models no longer just generate text but actively solve problems, manage tasks, and interact with external environments to drive tangible results
In Part 1, we explored the limitations of the scaling law, data constraints, and the legal challenges shaping AI development. Discover why AI is shifting from brute-force pre-training to inference-driven intelligence. Read Part 1 to uncover the full story!
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DeepSeek-R1 has achieved outstanding performance across various benchmarks, drawing global attention. In some metrics, it has even surpassed GPT-o1, and discussions about its relatively low training costs are actively taking place. However, focusing solely on performance and cost risks overlooking the deeper significance of this model.
The key takeaway is that DeepSeek-R1 represents a shift in the AI paradigm. AI technology is moving beyond large-scale pretraining based on data and toward enhancing continuous reasoning and problem-solving capabilities. DeepSeek-R1 is just one example of this trend. Other cutting-edge models like GPT-o1 and Claude-Sonnet 3.5 are also optimizing real-time reasoning through post-training and reinforcement learning, which will be crucial for future AI advancements.
Therefore, rather than limiting our attention to individual performance metrics or cost concerns, we should focus on the broader changes driven by this inference-centered paradigm. These reasoning-based AI models will have a profound impact on how we work and live. This shift is not just about improving training efficiency but about expanding AI’s ability to autonomously solve complex problems.
The evolution of inference-centered AI is closely linked to the emerging trend of Agent AI. Unlike traditional AI that merely provides information, Agent AI is an autonomous system capable of planning and executing tasks to achieve specific goals. These agents solve complex problems through planning, tool utilization, and decision-making, while also interacting with external systems when needed.
By continuously performing tasks based on reasoning and judgment, Agent AI has the potential to revolutionize how we work and live. For instance, it can deeply analyze customer issues, propose appropriate solutions, and autonomously determine follow-up actions—showcasing a key application of this technology.
Before DeepSeek-R1, DeepSeek-V3 pioneered key advancements in AI reasoning, automation, and decision-making—laying the groundwork for today’s AI agents. Understanding these innovations is crucial to grasping how AI is evolving beyond traditional models.
🔗 Explore DeepSeek-V3 and see how it’s shaping the future of AI agents!
Agent AI can interact with various systems by calling external APIs and plugins. This enables real-time data retrieval and ensures continuity in task execution.
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Agent AI can automatically call external tools as needed, enhancing work speed and efficiency.
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Agent AI systematically manages complex tasks by prioritizing and executing them step by step.
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Agent AI continuously improves by learning new patterns during operation, optimizing efficiency through reinforcement learning.
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Allganize is developing comprehensive Agent AI capabilities applicable to various business environments. This technology aims to deeply analyze user challenges, autonomously determine necessary data, access external sources, and automate tasks to deliver optimal solutions.
One example of this is the Agent-based Generative BI solution.
Generative BI is a solution that analyzes financial and business data in real time to generate automated reports. It autonomously determines how to access external sources, such as databases, and provides appropriate responses instantly. Users can retrieve data and generate reports using natural language commands without needing developer assistance.
Generative BI quickly analyzes key business data in response to user requests. The AI autonomously determines which external sources to access and retrieves the necessary data accordingly.
For example, if a user requests, "Analyze the budget versus actual performance from last fiscal year's cash flow statement," the system instantly extracts relevant information from multiple database tables and provides an analysis comparing budgeted and actual performance.
Generative BI enables executives to make quick decisions and respond immediately by automating real-time data access and analysis.
Generative BI significantly enhances productivity by automating repetitive tasks such as monthly or quarterly financial report generation. Users can simply input commands like "Generate the 2023 cash flow report," and the system will instantly produce the required report. Additionally, users can easily customize the report by modifying its concept as needed.
This reduces the time spent on data processing, allowing employees to focus on more strategic tasks.
Generative BI provides report templates and dashboards tailored to each company's needs. Key metrics are visualized through charts and tables, enhancing clarity and enabling executives to quickly grasp essential insights. Notably, the entire process is precisely customized based on user-defined natural language instructions.
Generative BI is a comprehensive solution that showcases the core capabilities of Agent AI, including natural language understanding, automated data analysis, and seamless integration with external systems. The AI’s ability to autonomously determine the appropriate external sources and retrieve data based on user requests highlights the strength of Agent AI technology.
Through this, companies can enhance real-time insights, strengthen data-driven decision-making, and automate repetitive tasks to maximize operational efficiency.
Allganize’s Generative BI serves as a prime example of how Agent AI technology can boost a company’s competitiveness.
Agent AI solutions help reduce the workload of office employees, create a more efficient work environment, and improve productivity and job satisfaction.
The case of DeepSeek-R1 demonstrates a paradigm shift in AI, moving from pre-trained models to inference-centered approaches. This transformation signals AI’s evolution from a mere information provider to an autonomous system that supports problem-solving and decision-making.
Allganize is keeping pace with this shift by strengthening its customers' competitiveness through Agent AI solutions. We will continue to offer tailored services and respond swiftly to market demands as AI technology evolves. As the era of inference-driven AI fully takes shape, our solutions will be a key partner in ensuring customer success.
Whether you're looking to integrate cutting-edge AI into your business or explore how DeepSeek models can optimize your operations, we’re here to help.
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