Agentic Hardware Design as Repository-Level Code Evolution

What Happened

Researchers have introduced HORIZON, a novel agentic framework that reimagines hardware design as a repository-level code evolution problem. Rather than treating hardware development as a series of manual, human-driven steps, HORIZON compiles a Markdown-based specification into a self-contained "project pack" that includes domain knowledge, an executable evaluator, and an acceptance predicate. The agent then iteratively evolves the hardware design code — treating the entire repository as a living artifact — until it meets the acceptance criteria.

This represents a shift from traditional hardware description language (HDL) workflows, where engineers manually write and debug Verilog or VHDL code. Instead, HORIZON automates the iterative refinement loop, using the evaluator to test each candidate design against the specification and the acceptance predicate to determine when the design is complete.

Why It Matters

The significance lies in how HORIZON reframes hardware design. Hardware development has long lagged behind software in automation, largely because the design space is constrained by physical synthesis, timing closure, and manufacturing rules. By treating hardware design as code evolution, the framework opens the door to applying software-era techniques — continuous integration, automated testing, and agentic iteration — to hardware workflows.

For the AI industry, this is notable for three reasons:

• Bridging the software-hardware automation gap. Software engineering has benefited from LLM-based code generation, but hardware design has remained resistant due to its stricter correctness requirements. HORIZON’s evaluator-based approach provides a structured feedback loop that could make AI-assisted hardware design more reliable.

• Repository-level reasoning. Most code generation agents operate at the function or file level. HORIZON operates at the repository level, meaning it must understand dependencies across modules, testbenches, and configuration files — a more realistic and challenging setting.

• Self-evolution without human intervention. The acceptance predicate allows the agent to autonomously determine when a design is "good enough," reducing the need for constant human oversight. This is a step toward truly autonomous design agents.

Implications for AI Practitioners

For those building agentic systems, HORIZON offers a concrete architecture worth studying. The Markdown harness as a specification format is elegant — it lowers the barrier to entry for defining design goals without requiring a formal verification language. Practitioners working on code generation agents should note how the project pack bundles domain knowledge with evaluation logic, creating a self-contained environment for iterative improvement.

The framework also highlights a key challenge: designing effective acceptance predicates. In software, unit tests serve this role, but hardware evaluation involves timing, area, and power constraints. Practitioners will need to think carefully about what "done" means in their domain and how to encode that as a pass/fail criterion.

Finally, HORIZON suggests that the next frontier for AI agents is not just generating code, but managing entire design workflows — from specification to verification. This has implications for tooling, evaluation benchmarks, and the skills required to build production-grade agents.

Key Takeaways

• HORIZON treats hardware design as repository-level code evolution, using a Markdown specification and an automated evaluator to iteratively refine HDL code.
• The framework bridges the automation gap between software and hardware design, applying agentic iteration to a domain traditionally resistant to such approaches.
• For AI practitioners, the architecture — particularly the project pack and acceptance predicate — provides a template for building self-evolving agents in other constrained design domains.
• The approach underscores the importance of defining clear, automated acceptance criteria for agentic systems, moving beyond simple code generation toward autonomous design completion.