Agentic RTL Debugger: Practical Exploration of AI Agents in Hardware Verification

This article introduces the open-source project Agentic RTL Debugger, which brings agentic AI workflows into the field of hardware RTL verification. Addressing the problem that traditional verification methods (relying on manual test cases and assertions) are struggling due to the exponential growth in chip design complexity, it explores solutions where AI agents autonomously analyze, diagnose, and fix hardware design errors, aiming to solve the pain point of time-consuming and expensive hardware verification.

Modern chip designs contain billions of transistors, and verification accounts for over 70% of the total project duration. When simulation tests fail, engineers need to analyze logs to locate signals, trace code paths, understand design deviations, and verify repair plans—this process relies on experience and is prone to oversight. Additionally, some errors are only triggered under specific timing conditions, making reproduction and analysis extremely challenging.

Agentic AI emphasizes agents' autonomous decision-making and tool usage capabilities, adapting to complex debugging tasks through multiple rounds of interaction (observing status, formulating plans, calling tools, evaluating results, adjusting strategies). The core components of the system include:

1. Environment Perception Layer: Integrates data sources such as simulation waveforms, test logs, RTL code libraries, and design specifications;
2. Reasoning and Planning Engine: Based on large language models, formulates strategies according to error types (timing/functional/interface/initialization), and decomposes tasks using chain-of-thought;
3. Tool Call Interface: Standardized calls to tools like code search, waveform query, simulation control, formal verification, and patch generation;
4. Feedback Loop Mechanism: Evaluates tool call results and adjusts strategies in a closed loop until the task is completed.

Taking timing violations as an example, the agent autonomously completes the debugging loop:

1. Error Identification: Detect setup time violations from simulation logs;
2. Signal Tracing: Query the waveform database to locate the violating register;
3. Path Analysis: Search combinational logic paths to identify critical delays;
4. Root Cause Localization: Discover that clock gating logic introduces additional delays;
5. Solution Generation: Propose suggestions to optimize gating logic or adjust clock frequency;
6. Verification Execution: Apply the patch and re-simulate to confirm the fix.

Advantages:

• Efficiency Improvement: Runs 24/7, analyzes multiple errors in parallel, and shortens debugging cycles;
• Knowledge Precipitation: Records successful cases to form reusable diagnostic patterns;
• Consistency: Eliminates human oversight and fatigue, improving result reliability;
• Scalability: Quickly integrates new error types or verification tools.

Limitations:

• Complex Architecture Understanding: Requires manual guidance for system-level architecture decision errors;
• Creative Fixes: AI solutions may not be as elegant as those from senior engineers;
• Toolchain Dependence: Relies on specific EDA tools, and cross-platform porting requires additional work.

Application Scenarios: Can be extended to regression test screening (dynamically selecting test subsets), coverage optimization (generating targeted test stimuli), design migration assistance (identifying and fixing incompatible code), and formal verification guidance (improving scalability).

Participation Methods: The project is implemented in Python, relying on LLM APIs and EDA tool interfaces, supporting configuration to connect different simulators and language models; the community expands hardware description languages (Verilog, SystemVerilog, etc.) and verification methodologies. Hardware engineers can learn how AI changes workflows, and AI researchers can use hardware verification scenarios to test Agentic AI capabilities.

Agentic RTL Debugger demonstrates the potential of AI agents in professional engineering fields. Although fully autonomous verification still takes time, intelligent debugging with human-machine collaboration is within reach. The maturity of such projects will redefine the role of hardware engineers, freeing them from tedious debugging work to focus on creative architecture design.