Enterprise AI Teardown: Schneider's "Generative to Agentic AI" Survey
An in-depth analysis from OwnYourAI.com on the pivotal research by Johannes Schneider. We break down the transition from Generative to Agentic AI, translating academic concepts into actionable enterprise strategies and custom solutions.
Paper at a Glance
Title: Generative to Agentic AI: Survey, Conceptualization, and Challenges
Author: Johannes Schneider
Core Insight: This paper articulates the evolution from Generative AI (GenAI), known for content creation, to Agentic AI, a new paradigm defined by its ability to reason, plan, interact with its environment, and act autonomously to achieve complex, multi-step goals. Our analysis confirms this is not just an incremental update; it's a fundamental shift that unlocks unprecedented value for enterprises ready to move beyond simple chatbots and content generators.
Section 1: The Foundational Shift - From Assistants to Autonomous Actors
Schneider's research clearly delineates between two eras of AI. GenAI, popularized by tools like ChatGPT, excels at responding to prompts. Agentic AI, however, is designed to understand objectives and execute tasks. For an enterprise, this is the difference between an AI that can write an email draft and an AI that can manage the entire marketing campaign: from analyzing market data, to drafting and sending emails, tracking responses, and scheduling follow-up meetings.
GenAI vs. Agentic AI: The Enterprise View
The distinction is critical for strategic planning. While GenAI offers productivity gains, Agentic AI promises process automation and transformation. The following table, inspired by the paper's analysis, outlines the key differences from a business application perspective.
Section 2: Quantifying the Leap - Performance That Drives ROI
The paper highlights performance benchmarks that aren't just academic; they are direct indicators of an AI's ability to solve real-world business problems. Agentic models are shown to dramatically outperform their generative predecessors on tasks requiring logic, planning, and abstract reasoning.
Case Study: The ARC Challenge (Abstract Reasoning)
The Abstraction and Reasoning Corpus (ARC) contains problems that are simple for humans but have historically been impossible for AI. Schneider's paper references models like OpenAI's o1 and o3, which, through agentic reasoning, show massive performance gains. For a business, this translates to an AI that can solve novel problems in logistics, engineering, or process optimization without needing to be explicitly trained on every possible scenario.
Efficiency & Cost: Smarter, Not Just Bigger
A key enterprise takeaway, supported by data in the paper (Figure 6), is that Agentic AI can be more cost-effective. A smaller model endowed with advanced reasoning abilities (like planning and self-correction) can outperform a much larger, more expensive model that relies on brute-force computation. This means custom, lean, and powerful AI solutions are now more accessible.
Section 3: The Enterprise Agentic Toolkit - Core Components
Agentic AI is not a single technology but a framework of interconnected capabilities. Understanding these components, as detailed by Schneider, is key to designing effective custom enterprise solutions.
Section 4: Building & Deploying Custom AI Agents for Your Enterprise
The true power of Agentic AI is realized through customization. The paper discusses how agents can be defined with specific roles, goals, and constraints. At OwnYourAI.com, we transform this concept into a practical service, building agents that are perfectly aligned with your business processes and objectives.
Interactive: Agent Profile Builder
See how a custom agent is defined. Select an enterprise role to generate a high-level agent profile, illustrating how we can tailor AI for specific business functions. This is inspired by the agent definition examples in the paper.
Section 5: Calculate Your Potential ROI with Agentic AI
Moving from theory to practice requires a clear understanding of the potential return on investment. Agentic AI drives ROI by automating complex, time-consuming tasks, reducing errors, and freeing up human experts to focus on high-value strategic work. Use our calculator, based on the efficiency principles in the paper, to estimate your potential savings.
Section 6: Knowledge Check - Are You Ready for Agentic AI?
Test your understanding of the key concepts from our analysis of Schneider's paper. This quick quiz will help solidify the core differences and opportunities presented by Agentic AI.
Conclusion: The Agentic Future is Custom-Built
The "Generative to Agentic AI" survey by Johannes Schneider is a landmark paper that signals a new chapter in artificial intelligence. The paradigm has shifted from content creation to autonomous task execution. For enterprises, this means the opportunity to build truly intelligent systems that can reason, adapt, and drive business outcomes with minimal supervision.
Off-the-shelf solutions will only scratch the surface of what's possible. The real competitive advantage lies in developing custom AI agents that are deeply integrated with your unique data, workflows, and strategic goals. At OwnYourAI.com, we specialize in translating this cutting-edge research into bespoke, high-ROI enterprise solutions.
Ready to build your autonomous enterprise?
Let's discuss how we can apply these agentic principles to solve your most complex challenges.
Book Your Free ConsultationReady to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat