Agentic AI vs generative AI: why the futures not just smarter its bolder

Microsoft’s Azure AI Foundry is at the forefront of AI, offering a unified platform for designing, customizing, managing, and supporting enterprise-grade AI applications and agents at scale. The recent introduction of models like GPT-4.5 from Azure OpenAI and Phi-4 from Microsoft showcase significant advancements in natural language processing and machine learning. These models provide more accurate and reliable responses, reducing hallucination rates and enhancing human alignment. These specialized LLMs ensure that AI agents understand and adhere to complex regulatory frameworks, accounting principles, and compliance requirements specific to the financial industry.

• About three-quarters of respondents (75%) said they intend to deploy AI agents to tackle tasks such as generating and iteratively improving code.
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• Additionally, the enterprise agent upgrades facilitate the creation of more robust and versatile AI agents, capable of handling complex tasks and workflows.
• Like a successful orchestra, this can’t happen without everyone playing from the same sheet music, providing the equivalent of well-defined guardrails that ensure reliability, accountability and compliance for every note played.
• As we journey through 2025, these innovations are reshaping technology and revolutionizing business operations and strategies.

Artificial Intelligence

Every successful Agentic AI factory implementation will need to invest in Agent architecture capabilities across their entire development ecosystem. McKinsey has published an operating model for Agentic AI Factories in their blog post. The no-code platforms like LyzrAI, n8n, Katonic, Smolagents, Lovable and Replit are simplifying the build complex multi-agent or multi-workflow systems inside the Agentic AI Factory.

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Meta’s foundation model on compiler optimization is notable because of effectiveness in code and compiler optimization. In another example, a vendor, Qventus, offers a customer-facing AI-based assistant called the Patient Concierge, which calls patients and reminds them of appointments, reiterates pre- and post-op guidelines, and answers general care questions.

In the technology world, and especially in AI, it’s practically sprinting. Learn more about accelerating agentic workflows with Azure AI Foundry, NVIDIA NIM, and NVIDIA AgentIQ. Credential hijacking and abuse leading to unauthorised control and data theft. And now I have to say, “If it sounds like me and asks for money, it’s not me.” Imagine now I’m a CEO. This goes beyond business email compromise — this is business everything compromise.

This progression isn’t about discarding earlier outputs but integrating them. AI agents incorporate intelligence as their cognitive foundation and utilize tokens as their expressive mechanism, but transcend both by adding the critical dimension of action. The true winners in the agentic AI revolution won’t be the companies with the most agents—they’ll be the ones whose agents are most effectively orchestrated to deliver consistent, reliable outcomes. If you’ve ever played around with any LLM like ChatGPT, try to ask it the same question twice and see what happens. While this creative flexibility is wonderfully useful in tasks like content generation, it’s wholly unacceptable in any mission-critical business process.

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However, public research on audio recognition and emotional audio generation remains limited. Get insights and exclusive content from the world of business and finance that you can trust, delivered to your inbox. The future of AI factories isn’t just technical—it’s democratized, collaborative and fundamentally human-centric in its design, ensuring that anyone with domain expertise can contribute to the agent economy regardless of their coding background. It’s this final capability—turning understanding into action—that makes agents the highest-order output of AI factories. When McKinsey projects that AI could add $4.4 trillion annually to the global economy, they’re not referring to passive intelligence or token production alone, but to the automated execution that agents enable across industries.

Deploy agentic systems in clearly defined areas where success can be measured. Execute well, then use those success stories to justify broader investments. Tangible wins can demonstrate ROI and build the case for confident adoption.

The next stage of generative AI is likely to be even “more transformative”, Yee and her colleagues suggested. Azure AI Foundry also simplifies the process of customization and fine-tuning, allowing businesses to tailor AI solutions to their specific needs. The platform’s integration with tools like GitHub and Visual Studio Code streamlines the development process, making it accessible for developers and IT professionals alike. Additionally, the enterprise agent upgrades facilitate the creation of more robust and versatile AI agents, capable of handling complex tasks and workflows. Multimodal capabilities, which allow agents to process and respond to various forms of input (text, voice, images), are also becoming more sophisticated, enabling seamless and natural interactions. Even more exciting, emotional intelligence in AI agents is gaining traction.

AI-based agents represent the “next frontier” of AI, according to a report from consultant McKinsey. The report predicts the influence of agentic systems — defined as “digital systems that can independently interact in a dynamic world” — will increase. The AI landscape is in constant transformation, fueled by breakthroughs in AI agents, cutting-edge platforms like Azure AI Foundry, and NVIDIA’s robust infrastructure.