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Evaluate an AI coding agent for chip design by testing whether it can complete the specific hardware job you need—including tool use, debugging, and independent verification—not just whether it can produce plausible RTL from a prompt. A useful evaluation fixes the tasks and tool environment, records every attempt and intervention, and scores functional results separately from speed, cost, and workflow breadth.
How do I evaluate AI coding agents for chip design?
Start by defining the job. “Write RTL” can mean anything from completing a line of code to turning a specification into a verified module or completing an implementation flow. These are different capabilities; combining them into one score can conceal where an agent succeeds or fails.
1. Define the task you actually need
Choose one or more task categories and keep their results separate:
- Specification-to-RTL generation or code completion.
- RTL modification, module reuse, or lint and quality-of-results (QoR) improvement.
- Testbench, assertion, or other verification-artifact generation.
- Debugging a failing design using compiler, simulator, lint, formal, or waveform feedback.
- Repository-level maintenance, including locating and repairing a multi-file issue.
- End-to-end EDA automation, such as completing synthesis, physical design, or an RTL-to-GDS flow.
For each category, define the input, expected output, pass criteria, and permitted tools. A narrowly scoped module-generation task should not be treated as evidence of repository-level repair or physical-design capability.
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2. Decide what counts as a pass
Set acceptance criteria before running agents. At minimum, distinguish whether the design compiles, whether it passes the supplied simulation, and whether it satisfies independent tests or formal properties appropriate to the specification. A simulation pass only establishes that the tested behaviors passed; it does not prove that the complete specification is satisfied.
For implementation tasks, specify the required downstream stages and completion criteria. If PPA or another implementation metric matters, record the technology libraries, EDA toolchain, constraints, and measurement conditions. A result from one design and flow is not evidence that the agent can complete other commercial tape-out flows.
3. Make runs reproducible and fair
Pin source revisions, tool versions, libraries, prompts and specifications, constraints, and random seeds where applicable. Give each system equivalent access to source hierarchy, documentation, diagnostics, and debugging artifacts. Record permissions and use a sandbox when an agent can execute commands or modify source files.
Set the interaction budget in advance: number of attempts, retry policy, time limit, and whether the agent may inspect test results or reference material. Keep these conditions constant across systems. For an evaluation intended to predict local use, preserve the conventions and tool access the team would actually allow.
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4. Measure more than pass rate
Report functional correctness and independent verification outcomes alongside operational measures. Depending on the task, track compile and simulation success, formal-check results, testbench or assertion quality, repair success after diagnostics, preservation of passing regressions, downstream flow completion, wall-clock time, token or runtime expenditure, and human intervention.
Break results out by task category. Include all attempts, invalid outputs, timeouts, and failures, plus retry policies and interaction budgets. Report uncertainty or confidence intervals when the sample size supports them, and include representative failure types—such as hierarchy navigation, state-machine behavior, assertions, or coordinated multi-file changes. A single average can hide a weakness that matters to your design team.
5. Reserve held-out tasks
Use tasks that were not exposed to the evaluated system where possible, and keep local private tasks for validation. Reference patches and solutions can create contamination risk. NVIDIA Labs says the initial public CVDP release excludes reference outputs and patches to reduce that risk; its repository also notes that 20 datapoints were omitted because of test-harness issues or licensing restrictions. Record the exact release and dataset used rather than treating the benchmark name alone as a complete description.
Which benchmark should I use for RTL coding agents?
Choose a benchmark whose task scope matches the claim you want to test. CVDP, Phoenix-bench, FluxBench, and ASIC-Agent-Bench address different parts of hardware work; their scores are not interchangeable.
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| Benchmark | Best fit | Scope and qualification |
|---|---|---|
| CVDP | Broad RTL design and verification tasks | NVIDIA Labs’ hardware verification and RTL benchmark framework covers practical design and verification task families, including testbench and assertion work. Check the exact release and dataset notes. |
| Phoenix-bench | Repository-level hardware issue resolution | The 2026 preprint describes 511 verified Verilator instances from 114 GitHub repositories, targeting execution-grounded issue repair in pinned environments. |
| FluxBench | Tool-interactive EDA workflows | The 2026 preprint evaluates workflows including RTL generation and repair, synthesis, placement and routing, engineering-change-order work, and RTL-to-GDS under shared prompts, tool environments, and technology libraries. |
| ASIC-Agent / ASIC-Agent-Bench | Autonomous ASIC-design tasks and multi-agent workflows | The 2025 preprint describes a sandboxed system with separate RTL-generation, verification, OpenLane hardening, and Caravel-integration roles, and introduces a benchmark for agentic ASIC design tasks. |
Read current task definitions and release notes before adopting any suite. Benchmark versions, task mixes, harnesses, and attempt budgets can change; comparing scores across different conditions as though they were a single leaderboard is not sound.
What published results can—and cannot—tell you
NVIDIA’s 2026 technical blog reports that ACE-RTL with Nemotron 3 Ultra achieved a 97.1% average pass rate across nine CVDP categories, compared with 95.2% for Kimi K2.6 and 92.1% for GLM 5.2. These are vendor-published results on NVIDIA’s CVDP setup, not independently validated estimates of success on a company’s production RTL. The article describes ACE-RTL as an agent pattern with generator, reflector, and coordinator roles that iterates through generation, checks, failure analysis, and subsequent attempts. Read NVIDIA’s account of CVDP and ACE-RTL.
Phoenix-bench’s 2026 paper authors report that one round of testbench-log feedback raised 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 their benchmark setup. This is evidence that feedback can matter in that setup, not a promised improvement for other agents or design repositories. FluxBench’s authors report up to an 86.27% performance gap between agent-system architectures using the same foundation model under their evaluation setup; the finding is a reason to evaluate the complete agent system, not only its underlying model.
Can AI agents write and debug RTL reliably?
Reliability depends on the task and the evidence collected. An agent may generate syntactically plausible RTL yet fail to meet the specification, or produce a working first draft but struggle to interpret a diagnostic and make a safe repair. Test both initial output and the iterative tool loop: compile or simulate, inspect failures, make a targeted change, then rerun checks and regressions.
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NVIDIA’s Developer Blog describes the iterative nature of this work: “Engineers rarely solve complex RTL tasks in one attempt; they iterate with compilers, simulators, lint tools, waveform inspection, and verification feedback.” Treat that as an organizational statement, not as a performance result. In your own evaluation, record which tools the agent can access and whether it can use their outputs without breaking previously passing behavior.
Include hierarchy and repository-level bugs
Hardware defects can cross module boundaries through signal flow, so a repair may require understanding how a signal propagates across hierarchy rather than editing the file nearest a failing test. Phoenix-bench focuses on this kind of repository-level work and highlights hierarchy-aware localization, FSM and control-flow defects, testbench bugs, and coordinated multi-file changes. It also uses pinned Verilator environments, making its results more relevant to repository repair than a prompt-only code-generation test.
Software repository benchmarks do not automatically transfer to RTL. Hardware repositories have distinct semantics, verification flows, and failure modes; a strong software issue-resolution score alone does not establish hardware repair ability.
Test feedback sensitivity, not just first-pass output
For every failed task, preserve the initial diagnostic and track what happens after feedback. Note whether the agent correctly identifies the failure, changes the relevant code, passes the original check, and preserves unrelated regressions. Include independent tests or formal properties when appropriate; the agent should not be judged solely by a testbench it generated itself.
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How do I compare AI agents for chip design?
Run candidates on the same task categories and controlled environment, then compare them on the dimensions that matter for the intended job. Weight the dimensions rather than declaring one universal winner.
| Comparison dimension | What to record |
|---|---|
| Correctness and verification | Specification-conformant behavior, independent test and formal results, and regression preservation. |
| Workflow breadth | Which stages it completes across RTL, verification, debugging, and relevant EDA flows. |
| Repository and hierarchy work | Ability to locate faults across files and modules and coordinate changes safely. |
| Feedback use | How it handles compiler, simulator, lint, formal, and waveform-related diagnostics across iterations. |
| Access and integration | Permitted context, documentation retrieval, EDA tools, source access, and operational permissions. |
| Efficiency and oversight | Completion rate, elapsed time, token or runtime cost, retries, and required human intervention. |
| Deployment and reproducibility | Data handling, access controls, environment repeatability, and ability to reproduce reported outcomes. |
Keep the task set, toolchain, agent and model configuration, attempt limits, and scoring rules with the results. If a system is strong at RTL drafting but weak at assertion generation or multi-file debugging, show that profile instead of hiding it in a composite average.
Separate product capabilities from comparative proof
Commercial product pages can help identify integration questions and claimed workflow coverage, but they do not establish an apples-to-apples performance comparison. Cadence describes ChipStack AI Super Agent capabilities spanning RTL generation, testbench creation, regression orchestration, debug, formal plans and SVA, UVM sequences, checkers, and coverage using its EDA tools. Siemens describes Fuse EDA AI Agent across architecture exploration, RTL coding, verification, physical implementation, sign-off, and manufacturing readiness. Those are vendor descriptions; confirm current availability, integrations, and workflow scope directly, then validate relevant capabilities in a controlled local pilot. Cadence ChipStack and Siemens Fuse provide their respective product descriptions.
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