Agentic AI: The Age of Reasoning | 20 Expert Interview Questions & Answers

📌 ABOUT THIS VIDEO What changes when AI moves beyond answering questions and begins to plan, use tools, respond to feedback, and take actions toward a goal? That is the promise—and the challenge—of agentic AI. In this research-based expert interview, we explore the ideas behind Agentic AI: The Age of Reasoning. Across 20 essential questions, we examine how autonomous agents work, how they evolved, and why greater autonomy does not automatically mean greater reliability. Based on a review by Nisa and colleagues, this video explains the core design patterns, agent types, operating environments, and open challenges shaping the field. From tool use and ReAct to planning, reflection, and multi-agent collaboration, we connect the concepts to the practical design of AI systems. 🎯 WHAT YOU'LL LEARN • Agentic AI Fundamentals: What makes an AI system an agent, and how it differs from classical, rule-based AI. • How Agents Work: The four-stage framework: Data, Analysis, Execution, and Adaptation. • Evolution of Agentic AI: Five broad phases, from early agent foundations and learning-based systems to modern multimodal and collaborative agents. • Core Agent Design Patterns: Tool use, reflection, ReAct, planning, and multi-agent collaboration. • Agent Types and Environments: Seven agent categories and how static or dynamic, observable or partially observable, deterministic or stochastic environments affect behavior. • Memory and Reasoning: Parametric, working, and external memory—and why reasoning can still fail through contradictions, missing dependencies, or causal errors. • Alignment, Safety, and Evaluation: Why objectives can be misspecified, why multi-step agents are difficult to benchmark, and how privacy, security, cost, and coordination complicate deployment. • Future Research Directions: Continual learning, robustness, human–AI collaboration, interoperability, and more reliable self-improving systems. 💡 KEY TAKEAWAY Agentic AI is a systems problem—not just an LLM problem. An agent operates through a loop of perception, reasoning, action, and feedback. Design patterns, memory, and the environment all shape what it can do, but the central challenge is building autonomy that remains reliable, safe, and aligned under uncertainty. 📚 RESEARCH REFERENCE Nisa, U., Shirazi, M., Saip, M. A., & Mohd Pozi, M. S. (2026). Agentic AI: The age of reasoning—A review. Journal of Automation and Intelligence, 5, 69–89. https://doi.org/10.1016/j.jai.2025.08... 🎓 WHO SHOULD WATCH? • AI and machine learning students • Aspiring AI engineers and Generative AI developers • Researchers exploring LLM agents, reasoning, and autonomous systems • Developers building tool-using or multi-agent workflows • Professionals preparing for AI and machine learning interviews • Anyone interested in the capabilities, limitations, and responsible deployment of agentic AI If you're exploring LLMs, AI agents, reasoning, planning, or multi-agent systems, this interview offers a structured, research-based overview of the field and the questions that remain open. 🔔 STAY CONNECTED Subscribe to AI With Marzieh for research-based AI explainers, expert-style technical interviews, Generative AI tutorials, and clear breakdowns of modern AI architectures. Like the video and share your thoughts in the comments: Which challenge do you think is most important for reliable AI agents—reasoning, memory, evaluation, or alignment? 🔎 HASHTAGS #AgenticAI #AIAgents #AutonomousAgents #AIReasoning #LLM #LLMAgents #GenerativeAI #ArtificialIntelligence #MachineLearning #AIEngineering #MultiAgentSystems #ReAct #AIPlanning #Reflection #ToolUse #AIAlignment #ResponsibleAI #AIEvaluation #HumanAI #DeepLearning #AIResearch #AIWithMarzieh