09-09, 10:00–10:45 (Asia/Jerusalem), Main Hall (30)
Language: English
see description
AI agents can plan, act, and adapt, or at least, they appear to. Under the hood, they remain fragile systems built on prediction rather than true understanding.
This talk explores what LLM agents are really capable of today, where they break, and how to design around their limits. Through real-world failure patterns and success stories, we’ll unpack the myths, highlight common traps, and share practical tools and evaluation techniques for building agents that are genuinely useful.
Finally, we’ll look ahead to how these systems may evolve, and what you can do right now to stay ahead as agent-powered development becomes the norm.
Basic
Target audience –Developers
Shir is the co-founder and CTO of Deepchecks, a startup for evaluation and continuous validation of AI-based applications. Previously, Shir worked at the Prime Minister’s Office and at Unit 8200, leading research in various Machine Learning and Cyber related problems. Shir has a B.Sc. in Physics from the Hebrew University, which she obtained as part of the Talpiot excellence program, and an M.Sc. in Electrical Engineering from Tel Aviv University. Shir was a featured honoree in the Forbes Europe 30 Under 30 list.