SILICON DESIGN · AI

Agentic Chip Design

Q: Can LLM agents write production RTL?

For the right tasks, usefully generating a module from a spec, refactoring, writing assertions and testbenches, and explaining or triaging a failure. What they cannot do is own correctness: their output is a draft that still goes through the same verification and review as human-written RTL. Treated as a fast, tireless junior engineer whose work is always checked, they add throughput; treated as an oracle, they add risk.

Q: How do you trust what an agent produces?

You do not trust it you verify it, with exactly the flow you already use. Agentic methods change who does the iterations, not what counts as signed off. Every agent-produced result passes the same timing, DRC, LVS, coverage and review gates as any other, and the human remains accountable at tapeout. The value is real precisely because it sits inside your existing verification, not around it.

AI in chip design has moved past autocomplete. Reinforcement learning now explores the placement and PPA space, LLM agents draft and debug RTL and testbenches, and orchestration agents drive the EDA flow through hundreds of runs unattended. Used well, it is a throughput multiplier on the tedious-but-skilled work. The engineer still sets the objective and owns the sign-off — the agent does the search, not the accountability.

RL optimisationDesign-space explorationLLM agentsEDA orchestrationHuman-in-the-loopYour IP, your flow

The loop What it means Where it helps Questions

An agent runs the design loop; the engineer sets the goal and owns the sign-off

WHAT IT MEANS

Three layers, not one buzzword.

“Agentic” covers three distinct capabilities that are often lumped together. They mature at different rates and carry different risks.

Learned optimisation — reinforcement learning and design-space exploration for placement, floorplanning and PPA, of the kind Google’s AlphaChip showed and tools such as Synopsys DSO.ai and Cadence Cerebrus productised. It searches more of the space than a human has time for, and sometimes finds a better corner.
LLM agents — models that draft a module from a spec, refactor RTL, write assertions and testbenches, and triage a failing run. Fast and tireless, but their output is a draft that still passes the usual verification — a junior engineer whose work is always checked, not an oracle.
Orchestration agents — an agent that plans a sequence of tool runs, reads the results, decides the next action and iterates, unattended, toward a goal you set. This is where “agentic” stops being a single model answering a prompt and becomes a system doing the loop.
The human boundary — across all three, the objective, the tradeoff decisions and the tapeout stay with the engineer. Agentic methods change who does the iterations, not what counts as signed off.

WHERE IT EARNS ITS PLACE

Search and throughput, not judgement.

Design-space exploration — sweeping synthesis and floorplan options overnight to find a better PPA point than a human would have time to try, then handing the shortlist back for a decision.
Tedious-but-skilled work — constraint tuning, ECO iteration, coverage closure and failure triage: real engineering that rewards patience and volume, which is exactly what an agent supplies.
Throughput — hundreds of runs in the time a team would do a handful, so the engineers spend their hours on the decisions rather than the iterations.
Where it does not help — yet — novel architecture, the call on which tradeoff to accept, and accountability for the result. Treating an agent as an oracle for any of these is where programmes get into trouble.

HOW WE ENGAGE

Inside your flow, not a black box.

Your environment, your IP — we bring agentic methods into your tools and your environment rather than shipping your RTL elsewhere. Design data, models and runs stay where your IP already lives.
Measured against a human baseline — an agent is worth using only where it beats the current approach on PPA, schedule or cost. We measure it, rather than adopt it on faith.
Verification unchanged — every agent-produced result passes the same timing, DRC, LVS, coverage and review gates as any other. The value comes from sitting inside your signoff, not around it.
Human-in-the-loop by design — the engineer sets the objective, reviews the shortlist and owns the tapeout. The agent is accountable for none of it, which is exactly why it can be trusted to do the search.

WHERE THIS APPLIES

Industries this serves.

Semiconductor Consumer Electronics Automotive & Mobility Telecommunications

COMMON QUESTIONS

What engineers ask about this.

Does agentic chip design replace engineers?

No, and the teams getting value from it are clear about why. An agent is good at search and at tedious-but-skilled work — exploring a design space, tuning constraints, closing coverage, triaging failures — run at a throughput no human matches. It is not good at the judgement calls: which architecture to take, which tradeoff to accept, and the accountability for a tapeout. The engineer sets the objective and owns the sign-off; the agent does the iterations in between.

What is reinforcement-learning placement?

Instead of a human or a fixed heuristic placing blocks, an RL agent learns a placement policy by trying many layouts and being rewarded for better wirelength, congestion and timing. Google's AlphaChip showed this for floorplanning, and design-space optimisation tools such as Synopsys DSO.ai and Cadence Cerebrus apply the same idea to the wider PPA space. The win is that it explores more of the space than a human has time to, and sometimes finds corners a human would not.

Can LLM agents write production RTL?

For the right tasks, usefully — generating a module from a spec, refactoring, writing assertions and testbenches, and explaining or triaging a failure. What they cannot do is own correctness: their output is a draft that still goes through the same verification and review as human-written RTL. Treated as a fast, tireless junior engineer whose work is always checked, they add throughput; treated as an oracle, they add risk.

How do you trust what an agent produces?

You do not trust it — you verify it, with exactly the flow you already use. Agentic methods change who does the iterations, not what counts as signed off. Every agent-produced result passes the same timing, DRC, LVS, coverage and review gates as any other, and the human remains accountable at tapeout. The value is real precisely because it sits inside your existing verification, not around it.

Does our IP leave our environment?

It should not have to, and that is a question to settle before any tool touches the design. We bring agentic methods into your flow and your environment rather than shipping your RTL to someone else's — design data, models and runs stay where your IP already lives. For many customers the confidentiality position is the deciding factor, so it is designed in from the start.

KEEP READING

Related work.

SERVICES

Bring us the difficult part.

If you want agentic methods in your flow without handing over your IP or your judgement, that is the engagement we are built for. Tell us the specification, the constraint and the deadline.

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Agentic Chip Design

Three layers, not one buzzword.

Search and throughput, not judgement.

Inside your flow, not a black box.

Industries this serves.

What engineers ask about this.

Does agentic chip design replace engineers?

What is reinforcement-learning placement?

Can LLM agents write production RTL?

How do you trust what an agent produces?

Does our IP leave our environment?

Related work.

Physical design & closure

Verification methodology

RTL to GDSII

AI silicon & acceleration

Post-silicon validation

Coverage, not passing tests

Bring us the difficult part.