EDAgent: An Intelligent Agent Assistant for Electronic Design Automation Research

This article introduces EDAgent, an intelligent agent tool for the EDA research domain. It automates research tasks such as code analysis, debugging, and experiment setup through skill-based decision logic and knowledge reuse mechanisms, and can be used without programming background. It aims to solve the problems of repetitive work and knowledge loss in EDA research, helping researchers focus on creative work.

EDA research involves a large number of repetitive tasks (such as code verification, experiment configuration, result analysis, etc.), which are both time-consuming and error-prone. More seriously, research experience and skills are often lost with personnel turnover; new members have to learn from scratch, and it is difficult to effectively pass on the experience of seniors. EDAgent is designed to solve these problems.

EDAgent is an AI agent platform specifically built for EDA research. It abstracts research tasks into reusable "skills" and matches skills to handle user requests through intelligent decision logic. Its core features include:

• Skill-based Architecture: Functions are encapsulated as independent skills that can be combined and reused
• Adaptive Learning: Optimizes decision logic based on feedback
• Knowledge Precipitation: Converts research experience into reusable assets
• Zero-code Interaction: Describe requirements in natural language without programming background

EDAgent has four preset core skills covering major EDA research scenarios:

1. Code Analysis Skill: Checks code quality (syntax errors, style consistency, performance bottlenecks, etc.) and generates detailed reports
2. Debugging Assistance Skill: Parses error logs, infers root causes, generates repair plans, and recommends regression tests
3. Experiment Setup Skill: Plans parameter spaces, generates batch tasks, optimizes resource scheduling, and organizes results
4. Workflow Automation Skill: Orchestrates multi-step tasks, handles conditional branches, recovers from exceptions, and tracks progress

The core of EDAgent lies in its skill scheduling mechanism, with the following process:

1. Intent Recognition: Parses user natural language to extract task type, target object, and constraints
2. Skill Matching: Filters candidates from the skill library (exact/fuzzy matching, combination recommendation)
3. Knowledge Injection: Injects historical cases, best practices, and environmental information when calling skills
4. Execution and Feedback: Provides real-time progress feedback, adaptively adjusts for exceptions, summarizes results, and records them for optimization

EDAgent plays a role in various scenarios:

• New Member Onboarding: Analyzes RTL design module dependencies, points out cross-clock domain risks, and helps new members get up to speed quickly
• Simulation Failure Troubleshooting: Parses GB-level logs, locates assertion failures and signal anomalies, and infers the causes of timing violations
• Batch Experiment Execution: Automatically generates 96 PTPX power analysis experiment configurations, executes them in parallel, and summarizes 3D relationship graphs

As a knowledge management platform, EDAgent precipitates knowledge through the following mechanisms:

1. Case Library: Records successful problem-solving cases (problem description, skill parameters, solution process, effect), and automatically recommends references for future use
2. Rule Engine: Encodes expert experience into explicit rules (e.g., when timing violation >10% and the critical path is at the cross-module interface, it is recommended to check constraint consistency)
3. Skill Evolution: Analyzes skill call success rates and feedback, and prompts maintainers to optimize skills

EDAgent promotes the intelligent and automated transformation of EDA research, allowing researchers to focus on creative work. Its deployment methods are flexible: desktop application, Web service, IDE plugin, CI/CD pipeline, which can be integrated into different workflows. It not only improves efficiency but also solves the problems of talent training and knowledge inheritance, making it a direction worth exploring for EDA teams to improve research efficiency.