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Adaptive Multi-Agent Framework for Automated Constraint Generation
DescriptionModern semiconductor design demands managing diverse constraints-- timing, power, CDC, RDC, UPF-- across multiple EDA flows. Manual writing, scripting and validation of these constraints is slow, error-prone, and inconsistent.

We propose a multi-agent, debate-driven system--orchestrated by GitHub Copilot-- to transform this process into intelligent automation. The system orchestrates constraint generation and validation through a structured refinement loop: Generate, Critique, Debate, Human-Feedback and Evolution. The system becomes an expert that understands our silicon context by integrating human expertise into an adaptive feedback loop-- rapidly prioritizing corrective actions based on real-time violations. Advanced context engineering methodologies like RAG, RLHF, and MCP ensure consistent context management for the agents and effective constraint generation across diverse tool environments.

This approach accelerates signoff, reduces errors, and scales seamlessly across heterogeneous constraint domains-- paving the way for intent-driven, intelligent design methodologies.