Uploaded February 2026 | Updated September 2026, 3 weeks ago
Building AI agents sounds magical until you actually ship one. Would you love to build an AI/agentic product? Join the next cohort of our Bootcamp: https://ai.science/products-services/llm-agents-bootcamp
In this video, we dive into the very real, very practical challenges that show up once an AI-powered system meets the real world. From unreliable agent performance and missing retry mechanisms to API rate limits, cost constraints, and failures at scale, this is an honest look at what breaks first.
We explore tricky tradeoffs like balancing content quality versus frequency, why running AI analysis less often can actually degrade results, and how difficult it is to turn subjective concepts like “usefulness” into quantitative scores. These aren’t theoretical problems. They’re the kinds of issues that surface when you go from a handful of users to thousands.
If you’re building AI products, agents, or automation systems, this talk highlights why early prototypes feel fine, but scaling exposes entirely new classes of failure. Expect candid lessons, open questions, and the reality of experimenting your way toward reliability.
Hashtags
Join our Slack channel: aisc-to.slack.com
Where else to find us:
linkedin.com/in/amirfzpr
aisc.substack.com
youtube.com/@ai-science
https://lu.ma/aisc-llm-school
maven.com/aggregate-intellect
#AIEngineering #AIAgents #StartupLessons #BuildingInPublic #LLMs #AIProducts #Scalability #FounderJourney
Building AI agents sounds magical until you actually ship one. Would you love to build an AI/agentic product? Join the next cohort of our Bootcamp: https://ai.science/products-services/llm-agents-bootcamp
In this video, we dive into the very real, very practical challenges that show up once an AI-powered system meets the real world. From unreliable agent performance and missing retry mechanisms to API rate limits, cost constraints, and failures at scale, this is an honest look at what breaks first.
We explore tricky tradeoffs like balancing content quality versus frequency, why running AI analysis less often can actually degrade results, and how difficult it is to turn subjective concepts like “usefulness” into quantitative scores. These aren’t theoretical problems. They’re the kinds of issues that surface when you go from a handful of users to thousands.
If you’re building AI products, agents, or automation systems, this talk highlights why early prototypes feel fine, but scaling exposes entirely new classes of failure. Expect candid lessons, open questions, and the reality of experimenting your way toward reliability.
Hashtags
Join our Slack channel: aisc-to.slack.com
Where else to find us:
linkedin.com/in/amirfzpr
aisc.substack.com
youtube.com/@ai-science
https://lu.ma/aisc-llm-school
maven.com/aggregate-intellect
#AIEngineering #AIAgents #StartupLessons #BuildingInPublic #LLMs #AIProducts #Scalability #FounderJourney
