Electronic design automation (EDA) workflows have been highly fragmented, requiring engineers to juggle heterogeneous representations, specialized tools, and iterative verification across multiple design stages. Large language models (LLMs) have introduced more general reasoning and code‑generation capabilities to EDA, yet most existing approaches focus on isolated tasks and struggle to plan, verify, and repair designs autonomously within executable engineering environments.\ \ ChipOmni is a knowledge‑enhanced, context‑evolving AI agent framework for EDA. It unifies multimodal understanding, dynamic planning, multi‑agent collaboration, and verification‑driven tool interaction into a single autonomous system. By continuously ingesting feedback from external engineering tools, the framework maintains an evolving task context, incorporates domain knowledge, and iteratively refines intermediate designs.\ \ Across benchmarks for analog circuits, RTL, testbenches, and formal‑assertion generation, ChipOmni outperforms evaluated baselines on multiple task‑specific metrics and solves all 30 AnalogCoder‑Pro tasks. In a long‑horizon case study, ChipOmni advances a natural‑language RISC‑V specification through three design levels to a micro‑SoC GDSII layout that passes physical sign‑off, without any human intervention after initialization.\ \ These results demonstrate that ChipOmni can coordinate verification‑driven design across heterogeneous tasks and sustain long‑horizon engineering workflows, shifting the paradigm from fragmented, tool‑centric processes toward agent‑centric, closed‑loop systems.\ \ Review