From this track
Performance at AI Scale: What We Learned at monday.com
Monday Mar 16 / 10:35AM GMT
For years, performance engineering at monday.com focused on both client and server: identifying shifting bottlenecks, optimizing data flow, and ensuring responsiveness at scale.
Eviathar Moussaffi
R&D Director and Site Lead @Monday
From Pilot to Impact: How AI Is Transforming Large‑Scale Engineering
Monday Mar 16 / 11:45AM GMT
In large, highly regulated enterprises like ING, adopting AI in engineering isn’t as simple as enabling a new tool — it’s a fundamental shift in how thousands of engineers design, build, and deliver software.
Yaping (Luna) Luo
Global Head of Developer Experience (DevEx) & System Engineering @ING
Avoid AI Concept Creep with a Knowledge Graph Created in Hours
Monday Mar 16 / 01:35PM GMT
Modern data lakes and streaming architectures are optimized for data Volume, Variety and Velocity, not for preserving the Meaning of the data.
Atanas Kiryakov
President @Graphwise
Peio Popov
VP of Business Operations USA @Graphwise
Maximising an Agentic AI Ecosystem: Trust, Control and Scale
Monday Mar 16 / 02:45PM GMT
Agentic AI is rapidly moving beyond individual assistants toward interconnected ecosystems of agents, tools, and platforms. This shift promises step‑change improvements in productivity and autonomy—but it also introduces new challenges around trust, control, and operational scale.
Jonathan Griffiths
Field CTO @Dynatrace
The Rise of Runtime Intelligence: practical Lessons in Shipping Agentic Engineering Code to Production
Monday Mar 16 / 03:55PM GMT
Agentic engineering works remarkably well in controlled environments.
May Walter
Co-Founder and CTO @Hud
Context Engineering: Building the Knowledge Layer AI Agents Need
Monday Mar 16 / 05:05PM GMT
Every AI coding tool can generate code. Very few can generate the right code for your organization — because they're missing context.
Brandon Waselnuk
Developer Relations @Unblocked
