Khaled Alashmouny
Founder & CEO
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Khaled Alashmouny is the founder and CEO of AIDAChip, where he's building Multiplayer AI for semiconductor design teams — AI systems where multiple agents coordinate across roles, tools, and organizational boundaries to tackle the alignment overhead that costs the industry billions annually.
Before founding AIDAChip, Khaled spent 20 years in semiconductor engineering, including 13 years leading analog/mixed-signal design at Apple. He designed circuits that shipped in products used by hundreds of millions of people, and watched firsthand as world-class engineers — working with the best tools money can buy — still lost months to specification drift, handoff failures, and cross-team misalignment. He holds 7 patents and has published 9 IEEE papers on analog/mixed-signal integrated circuits.
Khaled earned his PhD in Electrical Engineering from the University of Michigan, where his research focused on analog/mixed-signal integrated circuits and Neural Microsystems.
What If Your Chip Design Team Moved Like a Single Body?
Most agentic demos you've seen has a hidden assumption: one user, one session, one task. But what happens when the agent needs to coordinate with 30 other agents, across 10 disciplines, on a project that takes 12 months — where a single miscommunication costs $10-50M? Chip design is that problem. Only 14% of chips succeed on first silicon. The bottleneck isn't individual engineer speed — it's silent divergence between disciplines working from specs that drift without noticing.
We built a multiplayer AI on the Anthropic Agent SDK, connected through three alignment layers: a living spec graph (System of Intent) that propagates changes and detects conflicts in real time, a tribal knowledge layer (Memory) that compounds methodology across projects, and milestone-aware execution that drives EDA tools with full design context.
Each agent operates within strict spec-hierarchy boundaries enforced at the API level. Cross-agent invocations use structured tool calls with typed parameters, logged for full auditability.
We talked with 15 practitioners across 8 major semiconductor and EDA companies. The universal finding: teams need alignment infrastructure, not faster copilots. We'll also share what broke — because coordination tax applies to AI agents too, and the failure modes are surprisingly instructive.
This talk covers the multi-agent architecture, evaluation methodology, and lessons from deploying agentic AI in one of engineering's most complex coordination domains.
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