Jan 16
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Most companies are experimenting with AI agents but very few understand how these systems actually work or how to scale them effectively. In this video, we’ll break down the core foundations of Agentic AI, from AI workflows to AI agents and multi-agent systems so you can design solutions that are reliable, scalable, and cost-efficient. You’ll learn: The 3 types of Agentic AI: Workflows, Agents, and Multi-Agent Systems How to decide between predictability vs. autonomy in enterprise AI The “One-Big-Brain Bottleneck” and how to fix it Common technical and business pitfalls (like memory mismanagement and cost control) Real-world lessons from enterprise deployments across government and legal sectors By the end, you’ll understand how to architect AI systems that actually deliver measurable business value. Sources: https://learn.deeplearning.ai/courses/ai-agents-in-langgraph/lesson/c1l2c/build-an-agent-from-scratch https://huyenchip.com/2024/07/25/genai-platform.html https://blog.langchain.com/how-to-think-about-agent-frameworks/ https://langchain-ai.github.io/langgraph/concepts/multi_agent/#multi-agent-architectures https://www.mckinsey.com/capabilities/risk-and-resilience/our-insights/deploying-agentic-ai-with-safety-and-security-a-playbook-for-technology-leaders https://medium.com/@nirdiamant21/5-common-mistakes-when-scaling-ai-agents-d64a6cdd04dd Chapters: 0:00 Intro 0:49 Workflows 1:27 AI Agents 4:25 Multi-Agent Systems 6:34 Risks