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Course contents

  • Getting Started with AI for VLSI Engineers5
  • Just Enough AI: Models and Context15
  • Prompting13
  • Trust and Verification14
  • Using AI for Engineering Work17
  • Engineering Walkthroughs7
  • Practical Foundations for AI Workflows4
  • Engineering Data5
  • Retrieval-Augmented Generation6
  • Agents and Tool Use18
  • Agent Design8
    • Define the Task and Boundaries
    • Design the Architecture
    • Build Safe Read-Only Tools
    • Control the Decision Loop
    • Record State and Hand Over
    • Handle Errors and Budgets
    • Build the Starter Controller
    • Evaluate Your Assistant
  • MCP and Skills6
  • Multi-Agent Design6
  • Practical Capstone6
  • Tokens, Cost and Latency12
  • Agentic Workflows for Chip Teams18
  • Interview and Judgment10
  • AI Skills: Full Course Index
  • Working with AI Glossary
  • Working with AI Cheatsheet
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‹AI Skills for VLSI Engineers
  1. Topics
  2. AI Skills for VLSI Engineers
  3. Agent Design

Agent Design · Beginner

Agent Design

Build a bounded, read-only regression assistant from task definition through evaluation.

  • 8topics
  • 8lessons

Start with an engineering question you can audit: what does the supplied reset failure show, which specification rule applies, and what remains unknown? This section designs a guided regression assistant for RTL and DV engineers, with equivalent evidence habits for STA and physical design. It searches approved log snapshots, reads an approved specification excerpt, and retrieves approved run history. It cannot launch a regression, edit a file, or change a waiver.

Note

All runs, paths, revision labels, and responses in this section are synthetic teaching fixtures. The complete Python listing is a standard-library mechanics baseline with a scripted policy, NOT a true LLM agent. The model adapter is a contract for a separately approved integration, not a working hosted or local connection.

Lesson

Concrete deliverable

Define the Task and Boundaries

Turn a regression question into a small, reviewable evidence-gathering task.

Design the Architecture

Separate the decision maker, controller, tools, and evidence record.

Build Safe Read-Only Tools

Validate exact inputs and keep log contents outside the authority boundary.

Control the Decision Loop

Choose one bounded action at a time and stop with an honest status.

Record State and Hand Over

Preserve evidence, unknowns, and useful next checks without hidden reasoning.

Handle Errors and Budgets

Make denial, missing data, repetition, and deadlines visible outcomes.

Build the Starter Controller

Study a complete standard-library baseline and a separate model adapter contract.

Evaluate Your Assistant

Grade evidence quality, boundary enforcement, and useful abstention on held-out cases.

What You Build

The deliverable is an evidence packet with bounded tool calls, source references, explicit unknowns, and a stopping reason. You will learn which decisions belong in deterministic code and which could later be proposed by a model. No provider account, dependency, EDA license, real design data, or network access is needed to understand the baseline. Follow the lessons in order; the starter lab gathers the entire controller in one listing.

Check yourself Does a successful lesson run prove that an agent diagnoses silicon bugs?

No. At most, executing the baseline would demonstrate controller mechanics on synthetic fixtures. Diagnosis quality requires separate evaluation of a real model and representative approved cases.

Related topics

  • Up: Working with AI

  • Next: Define the Task and Boundaries

  • Previous: Tools, workflows and agents

  • Next section: MCP and Skills

PreviousNext: Define the Task and Boundaries

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See also

  • Getting Started with AI for VLSI Engineers
  • Just Enough AI: Models and Context
  • Prompting