Lessons From Building and Deploying AI Agents to Production

Описание: Over the last 8 months, I’ve been building AI agents and agentic applications to solve real-world problems in production environments. In this video, I share some of the most important lessons I’ve learned while building AI agents, including the difference between AI agents vs workflows, the importance of context and prompt engineering, and why observability is critical when deploying agents in production.

AI agents are rapidly becoming a key paradigm in modern software engineering. Unlike traditional workflows that follow predefined steps, AI agents can reason, plan, and execute tasks autonomously based on the context and tools provided to them.

If you're a developer, AI engineer, or builder exploring agentic AI systems, this video will help you understand the key concepts behind building reliable AI agents.

What is in this video

• The difference between AI Agents vs AI Workflows
• Why context engineering and prompt engineering matter
• How to monitor and debug AI agents in production
• Lessons learned after 8 months of building agentic applications
• Tools and frameworks for building AI agents (n8n, Mastra, and more)

Chapters
00:00 Introduction
00:34 AI Agents vs AI Workflows explained
02:00 Why context is king in AI systems
03:13 Observability and monitoring agents
04:28 Tools for building agentic applications (n8n, Mastra)
06:22 Conclusion