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Evaluate an AI coding agent for chip design by testing the work you actually expect it to do—not just whether it can produce plausible RTL from a prompt. A useful evaluation includes generation, modification, debugging, testbench or assertion work, and verification, with the agent allowed to use the relevant tools and respond to their output. Choose benchmarks that match the job, pin the environment, and report results by task category rather than relying on one headline pass rate.
How do I evaluate AI coding agents for chip design?
Start by defining the job, then measure whether the complete agent system can perform it safely and reproducibly. That system includes the model, its instructions and agent framework, the tools it can access, and the rules governing retries and human intervention. A model-only prompt test cannot show whether an agent can compile or simulate its work, understand a failure, make a targeted repair, and preserve behavior that already passed.
NVIDIA describes that practical cycle this way: “Engineers rarely solve complex RTL tasks in one attempt; they iterate with compilers, simulators, lint tools, waveform inspection, and verification feedback.” That observation supports evaluating the feedback loop, not assuming that any particular agent will improve simply because tools are available. NVIDIA Developer Blog
Define the task before choosing a score
Separate task types that exercise different abilities. For example, spec-to-RTL generation is not the same job as debugging an existing repository or writing assertions for a design. A useful evaluation might include several of these categories:
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- Generating RTL from a specification or completing an existing module.
- Modifying RTL, reusing modules, or improving lint results or quality of results (QoR).
- Debugging a failing design, fixing a repository issue, or maintaining regression behavior.
- Generating testbenches, tests, assertions, or verification plans.
- Completing downstream EDA stages, such as synthesis or implementation, when those are part of the expected job.
Do not combine unlike tasks into a single opaque average. An agent might be strong at short, self-contained module generation and weak at hierarchy-aware debugging. A category-level result makes that difference visible.
Make the checks independent of the agent
For each task, specify what counts as success before running the agent. Check specification-conformant behavior with tests the agent did not write, and use formal properties where they fit the requirement. A clean compile or a passing simulation is evidence only for the checks that ran; it does not prove that every requirement is satisfied. For implementation tasks, define the required stages and acceptance criteria, including relevant PPA measures, before comparing systems.
Can AI agents write and debug RTL reliably?
Reliability is task- and setup-dependent. A single successful generation demonstrates that an agent produced RTL that passed a particular check under a particular environment. It does not establish a general probability of success on production designs. To evaluate reliability, include both first attempts and tool-assisted repair, and check whether a change fixes the failure without breaking previously passing behavior.
Test the full tool loop
Give each agent equivalent access to the information it would have in the intended workflow: the relevant source hierarchy, specification, documentation, constraints, and compiler, simulator, lint, formal, or waveform-related diagnostics. Record whether it can:
- Run the appropriate check and identify the relevant failure.
- Trace the failure to the responsible behavior or files.
- Make a focused change rather than a broad rewrite that obscures the cause.
- Re-run the original check and relevant regression tests.
- Preserve earlier passing behavior and report any unresolved failures.
Measure these steps as outcomes, not as assumptions. An agent that passes only after repeated prompts, extensive manual steering, or uncounted retries has a different practical result from one that completes the same work within the declared interaction budget.
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Include hierarchy and multi-file failures
RTL bugs may cross module boundaries through signal flow, so a benchmark built around isolated code completion can miss important repository work. Phoenix-bench is an execution-grounded benchmark for hardware repository issues; its authors report 511 verified Verilator instances drawn from 114 GitHub repositories. The paper emphasizes hierarchy-aware localization, FSM and control-flow bugs, testbench bugs, and coordinated changes across files. Phoenix-bench paper
The paper also reports that one round of testbench-log feedback increased resolved rates by 44.0 percentage points for OpenAI Codex, 44.6 points for Claude Code, and 42.1 points for OpenHands+GPT-5.2 in its tested setup. These are benchmark-specific results, not a general expected improvement for other agents, tasks, or toolchains. They do show why an evaluation should distinguish an agent’s unaided attempt from its response to real diagnostic feedback. Phoenix-bench paper
Which benchmark should I use for RTL coding agents?
Choose the benchmark whose task definition resembles the claim you want to evaluate. Broad RTL and verification tasks, repository maintenance, and end-to-end EDA workflows are different scopes; no single score covers them all.
| Benchmark or system | Scope | Best fit | Important qualification |
|---|---|---|---|
| CVDP | Hardware verification and a range of Verilog design and verification tasks, including work involving testbenches and assertions. | Comparing performance across a broad set of RTL coding and verification tasks. | NVIDIA Labs says the initial public release omits 20 datapoints because of test-harness issues or licensing restrictions and withholds reference outputs or patches to reduce contamination. Record the exact release and dataset used. |
| Phoenix-bench | Repository-level hardware issue resolution in pinned Verilator environments. | Testing navigation, debugging, hierarchy-aware localization, and multi-file repairs against execution-grounded issues. | Its reported outcomes apply to the paper’s benchmark instances, setup, and interaction conditions; they are not directly interchangeable with scores from another suite. |
| FluxBench | Tool-interactive EDA tasks, including RTL generation and repair, synthesis, placement and routing, ECO work, and RTL-to-GDS flows. | Evaluating agents expected to use EDA tools across implementation stages, rather than only write RTL. | For physical-flow comparisons, identify the libraries, tool chain, constraints, and stage completion criteria used. The paper’s comparisons belong to its evaluation setup. |
| ASIC-Agent / ASIC-Agent-Bench | A sandboxed multi-agent ASIC design system and a benchmark introduced for autonomous ASIC design tasks. | Studying task decomposition and tool access across roles such as RTL generation, verification, OpenLane hardening, and Caravel integration. | It is an example of a multi-agent system and research benchmark; match its actual task definitions to the workflow you need to assess. |
Read the current task definitions and release notes before adopting a suite. Scores from different benchmark versions, task mixtures, harnesses, retry limits, or tool access policies should not be presented as a direct ranking.
Use CVDP for broad RTL and verification coverage
CVDP is a sensible starting point when the question spans multiple kinds of Verilog design and verification work. Its initial public release has explicit coverage and availability qualifications: NVIDIA Labs notes that 20 datapoints were omitted because of harness issues or licensing restrictions, while reference outputs and patches were excluded to reduce contamination. State the release used and avoid treating the published set as a complete census of chip-design work. CVDP repository
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Use Phoenix-bench for repository-level repair
Choose Phoenix-bench when the target capability is fixing existing hardware repositories rather than generating a small design from a clean prompt. Its verified Verilator instances and issue-oriented tasks make hierarchy, multi-file edits, and interaction with a testbench failure part of the evaluation. Phoenix-bench paper
Use FluxBench for tool-interactive EDA work
FluxBench is the closer fit when the claim involves an agent operating tools across a broader EDA flow, including implementation tasks. Its authors report up to an 86.27% performance gap between agent-system architectures using the same foundation model under their evaluation setup. Treat this as evidence that the surrounding system can matter—not as a universal estimate of the advantage any one architecture will deliver. FluxBench paper
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsHow do I compare AI agents for chip design?
Compare agents on the same tasks, environment, permissions, and interaction budget. Keep results separated by category, and make the tested configuration reproducible enough that another evaluator can understand what the scores mean.
Control what each agent can see and do
- Pin source revisions, tool versions, libraries, prompts or specifications, and constraints. Set random seeds where applicable.
- Give systems equivalent access to documentation, source hierarchy, tool output, and debugging artifacts.
- Declare whether agents may execute commands, edit files, retrieve documentation, or use other tools. Sandbox command execution and source changes.
- Fix the attempt limit, retry policy, interaction budget, and rules for human intervention before testing.
- Use held-out tasks where possible. Do not expose reference patches or expected outputs; keep private tasks for local validation when practical.
Report outcomes that matter to the intended job
A pass rate alone can hide why one system is more useful than another. Track, as relevant to the task:
- Functional correctness against the specification and independent tests.
- Compilation and simulation results, plus independent formal or other verification checks.
- Testbench and assertion quality, if the agent is expected to produce verification assets.
- Repair success after real diagnostics, regression preservation, and failures by category.
- Completion of required downstream EDA stages and PPA or other implementation measures when those are part of the job.
- Wall-clock time, runtime or token expenditure, invalid and timed-out attempts, and human intervention.
Report per-category pass rates, the number of attempts, retry policy, and uncertainty or confidence intervals when sample sizes permit. Include representative failure classes: for example, hierarchy errors, FSM or control-flow bugs, weak tests, regressions, and tool-flow failures. Do not conceal failed or invalid runs behind a best-of-many result.
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Separate model capability from agent-system capability
A comparison can change because of the foundation model, agent architecture, tools, prompts, context, or iteration policy. FluxBench’s same-foundation-model comparison is designed to examine system-architecture differences within its own setup; NVIDIA’s ACE-RTL article reports results for a coordinated agent approach as well as model comparisons. Neither makes an uncontrolled cross-benchmark ranking valid.
NVIDIA reports a 97.1% average pass rate for ACE-RTL with Nemotron 3 Ultra across nine CVDP categories, compared with 95.2% for Kimi K2.6 and 92.1% for GLM 5.2. These are NVIDIA-published evaluation results for its reported CVDP setup, not independent validation or a forecast of performance on a company’s production RTL. NVIDIA Developer Blog
How should I assess commercial chip-design agents?
Commercial product descriptions can help define what to test, but they are vendor claims rather than apples-to-apples comparative benchmarks. Cadence describes ChipStack as supporting orchestration across RTL generation, testbench creation, regression orchestration, debugging, formal plans and SVA, UVM sequences, checkers, and coverage with its EDA tools. Cadence ChipStack AI Super Agent
Siemens describes Fuse EDA AI Agent as spanning architecture exploration, RTL coding, verification, physical implementation, sign-off, and manufacturing readiness. Product scope and integration details can change; confirm current availability and supported workflow directly with Siemens. Siemens Fuse EDA AI Agent
Before making a procurement decision, run a representative pilot using your access controls, design conventions, tool stack, and evaluation rules. Compare delivered work and required human effort—not feature lists alone.
What a useful evaluation conclusion looks like
State what the system was tested on, what tools and permissions it had, how many attempts it received, which checks determined success, and where it failed. Then make a bounded conclusion—for example, that an agent completed a defined class of repository fixes under a pinned Verilator setup, or that it passed a specified suite of RTL and verification tasks. Avoid turning a benchmark result into a general claim about production success.
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