(AAIF Chicago) The State of AI: From Hype to Production-Ready Agentic Systems
The State of AI: From AI Evolution to Agentic Enterprise Development
AI has evolved from rule-based systems and machine learning to generative AI and increasingly autonomous agents.
But the next challenge isn't simply making AI more capable.
It's understanding what happens when AI becomes part of how we research, build, operate, and make decisions.
This AAIF Chicago event features Arunava (Ron) Majumdar, Founder & CEO of Research Net.ai and Open Development Platform and former Head of the IBM Center for Advanced Studies (USA), presenting his perspective on the State of AI and the transition toward agentic enterprise development.
The evening asks:
Where are we really going with AI—and what foundations do we need to build what comes next?
We’ll explore the evolution of AI agents, the limitations of LLM-based generation, AI inference economics, data privacy, sustainability, the AI investment cycle, and the engineering foundations needed for production-ready agentic systems.
What We'll Cover
🤖 From AI to Agentic AI
Arunava takes us through the evolution of AI—from the early foundations of artificial intelligence and rule-based systems through machine learning, deep learning, generative AI, and today's emerging agentic systems.
🧠 Where LLMs Fall Short
What happens when probabilistic generation meets real-world requirements?
The discussion explores hallucinations, model drift, generalization bias, RAG, continuous learning, human-in-the-loop approaches, and reinforcement learning with human feedback.
🔐 The Data & Privacy Challenge
As AI systems gain access to increasingly valuable information, privacy and control become foundational questions. Arunava explores data leakage, exfiltration, consent, surveillance, regulatory considerations, data boundaries, single-tenant environments, and control over model training and inference.
💰 The Economics of Always-On AI
Individual inference costs may be falling—but agentic systems can operate continuously, creating a very different cost equation.
The presentation examines the implications of agentic workloads, token consumption, infrastructure, and the potential shift toward local or on-premise inference.
🌱 The Sustainability Question
The AI race also has a physical footprint.
Arunava examines the growing infrastructure requirements behind AI and the challenge of understanding the energy and environmental impact of increasingly large-scale AI systems.
📈 Beyond the AI Hype Cycle
What happens when AI investment, adoption, and expectations move faster than the underlying foundations?
The presentation looks at the shift from GenAI hype toward AI-ready data, AI engineering, ModelOps, and agentic AI, while highlighting the risks created when organizations pursue agents without sufficient attention to use cases, risk tolerance, compliance, and auditability.
⚙️ What Production-Ready Agentic AI Requires
The conversation then moves from the problems to the engineering opportunity:
Open automation specifications. Specialized agents. Production-ready patterns. DevOps. Testing. Observability. Security. Scalable deployment. Natural-language understanding.
These become the building blocks for moving agentic AI from experimentation toward enterprise development.
🧩 From Agents to an Agentic Development Platform
Arunava will also share how his work with Research Net.ai and Open Development Platform approaches agentic development through reusable components, orchestration, observability, testing, integration, and specialized agents—including research, generation, messaging, legal, news, video, and sports-focused agents.
Speaker
Arunava (Ron) Majumdar
Founder & CEO — ResearchNet.ai & Open Development Platform
Former Head — IBM Center for Advanced Studies (USA)
Arunava brings 28+ years of experience in software architecture, design, development, AI, integration, and enterprise modernization. He has led architecture, design, development, and deployment across 50+ projects with 40+ Fortune 500 organizations, while also spearheading academic research initiatives with universities including Northwestern and the University of Chicago.
His current work focuses on using automation and agentic platforms to accelerate research and product development, while Open Development Platform provides an environment for academics, researchers, and professionals to collaborate and exchange ideas.
Who Should Come
AI engineers, architects, developers, researchers, enterprise technology leaders, and agentic AI builders will find this especially relevant.
Also welcome: founders, students, technology professionals, academics, and anyone interested in understanding where AI is heading.
No prior knowledge is required.
About AAIF
The Agentic AI Foundation (AAIF) is a community dedicated to advancing the understanding and practical application of agentic AI. We bring together engineers, researchers, founders, builders, and AI enthusiasts to explore how autonomous AI systems are designed, evaluated, and deployed.
Through reading groups, workshops, and community discussions, AAIF brings together engineers, researchers, founders, and AI enthusiasts to exchange ideas, challenge assumptions, and learn from one another.
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