Presented by Microsoft Azure and NVIDIA
Imagine a future where an AI agent not only books your next vacation but also helps provide a shopping list based on your destination, weather forecast, and the best deals from around the web. With another click the agent can make these purchases on your behalf and ensure they arrive in ample time before your flight leaves. You’ll never forget essentials like goggles or sunscreen again.
In just one year, AI and machine learning has soared to new heights with the emergence of advanced large language models, and domain specific small language models that can be deployed both on the cloud and the edge. While this kind of intelligence is the new baseline for what we expect in our applications, the future of enterprise AI lies in complex, multi-agent workflows that combine powerful models, intelligent agents and human guided decision-making. This market is moving fast. According to recent Deloitte research, 50% of companies using generative AI will launch agentic AI pilots or proofs of concept by 2027.
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. As we journey through 2025, these innovations are reshaping technology and revolutionizing business operations and strategies.
AI agents: proactive, personalized, and emotionally intelligent
AI agents have become integral to modern enterprises, not just enhancing productivity and efficiency, but unlocking new levels of value through intelligent decision-making and personalized experiences. The latest trends indicate a significant shift towards proactive AI agents that anticipate user needs and act autonomously. These agents are increasingly equipped with hyper-personalization capabilities, tailoring interactions based on individual preferences and behaviors.
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. By understanding and responding to human emotions, agents not only boost productivity but also meaningfully improve the quality of services — making interactions more personal, increasingly human, and ultimately more effective, particularly in areas like customer service and healthcare.
Azure AI Foundry: the agent factory empowering enterprise AI innovation
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.
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.
Case study: Air India
Air India, the nation’s flagship carrier, leveraged Azure AI Foundry to enhance its customer service operations. By updating its virtual assistant’s core natural language processing engine to the latest GPT models, Air India achieved 97% automation in handling customer queries, significantly reducing support costs and improving customer satisfaction. This transformation underscores the potential of Azure AI Foundry in driving operational efficiency and innovation. Learn more.
NVIDIA NIM and AgentIQ supercharge agentic AI workflows
Taking this even further, Microsoft and NVIDIA are bringing new efficiencies to enterprise AI with the integration of NVIDIA NIM microservices into Azure AI Foundry. These zero-config, pre-optimized microservices make it easy to deploy high-performance AI applications across a range of workloads—from LLMs to advanced analytics. With seamless Azure integration and enterprise-grade reliability, organizations can scale AI inference rapidly and cost-effectively.
According to NVIDIA, when Azure AI Agent Service is paired with NVIDIA AgentIQ, an open-source toolkit, developers can now profile and optimize teams of AI agents in real time to reduce latency, improve accuracy, and drive down compute costs. AgentIQ offers rich telemetry and performance tuning capabilities, allowing developers to dynamically enhance agent execution.
“The launch of NVIDIA NIM microservices in Azure AI Foundry offers a secure and efficient way for Epic to deploy open-source generative AI models that improve patient care, boost clinician and operational efficiency, and uncover new insights to drive medical innovation,” says Drew McCombs, vice president, cloud and analytics at Epic. “In collaboration with UW Health and UC San Diego Health, we’re also researching methods to evaluate clinical summaries with these advanced models. Together, we’re using the latest AI technology in ways that truly improve the lives of clinicians and patients.”
Performance and cost efficiency are further amplified by NVIDIA TensorRT-LLM optimizations, now applied to popular Meta Llama models on Azure AI Foundry. These include Llama 3.3 70B, 3.1 70B, 8B, and 405B, delivering immediate throughput and latency improvements—no configuration required.
Early adopters like Synopsys report transformative results: accelerated workloads, reduced infrastructure costs, and smoother deployment cycles. This performance uplift is powered by deep GPU-level optimizations enabling better GPU utilization and lower total cost of ownership.
“At Synopsys, we rely on cutting-edge AI models to drive innovation, and the optimized Meta Llama models on Azure AI Foundry have delivered exceptional performance,” says Arun Venkatachar, VP engineering, Synopsys Central Engineering. “We’ve seen substantial improvements in both throughput and latency, allowing us to accelerate our workloads while optimizing costs. These advancements make Azure AI Foundry an ideal platform for scaling AI applications efficiently.”
Whether you’re deploying serverless APIs or managing your own infrastructure with Azure virtual machines or Azure Kubernetes Service, developers can now flexibly build with NVIDIA’s inference stack—and get enterprise support through NVIDIA AI Enterprise on the Azure Marketplace.
NVIDIA infrastructure: powering the AI revolution
NVIDIA continues to lead the charge in AI infrastructure, with predictions indicating a shift towards quantum computing and liquid-cooled data centers. Quantum computing advancements, particularly in error correction techniques, promise to enhance computational power and efficiency, addressing instability issues that currently limit quantum hardware.
The transition to liquid cooling in data centers is another critical trend, driven by the need for higher performance and energy efficiency. This shift is accompanied by a transformation in data center architecture, moving towards integrated compute fabrics that facilitate communication between thousands of AI accelerators. NVIDIA’s dominance in AI hardware and software, bolstered by strategic partnerships with major companies, positions it as a key player in the enterprise AI sector.
Learn more about accelerating agentic workflows with Azure AI Foundry, NVIDIA NIM, and NVIDIA AgentIQ.
Mike Hulme is general manager, digital and app innovation, at Microsoft
Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact
