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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
  • MCP and Skills6
  • Multi-Agent Design6
  • Practical Capstone6
  • Tokens, Cost and Latency12
  • Agentic Workflows for Chip Teams18
    • Regression Triage Agent
    • Lint-and-Fix Loops
      • Anatomy of the Loop
      • Acceptance Rules and the Test Suite
      • Limits and Failure Modes
      • A Loop Simulator with a Fake Fixer
    • Guardrails and Human in the Loop
    • Evaluating and Operating Agents
  • Interview and Judgment10
  • AI Skills: Full Course Index
  • Working with AI Glossary
  • Working with AI Cheatsheet
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‹Lint-and-Fix Loops
  1. Topics
  2. AI Skills for VLSI Engineers
  3. Agentic Workflows for Chip Teams
  4. Lint-and-Fix Loops
  5. Anatomy of the Loop

Reference: Agentic Workflows for Chip Teams · Beginner to Intermediate

Anatomy of the Loop

The five stages of a lint-and-fix loop, with a schematic pass and a worked cost budget.

On this page

    The five stages

    Each round moves through the same five stages. The loop never edits the real checkout directly.

    Stage

    Input

    Output

    Lint

    The checkout or scratch copy

    A list of findings with file and line.

    Diagnose

    Findings plus surrounding code

    A prompt-sized bundle of context.

    Propose

    The bundle

    One patch from a model.

    Apply

    The patch

    A scratch copy with the patch applied.

    Rerun

    The scratch copy

    Fresh lint hits and test results.

    The rerun output feeds the accept decision. A kept patch becomes the baseline for the next round.

    A schematic pass

    The transcript below is schematic and illustrative. It is not output from any real system.

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    schematic transcript (illustrative only, never from a real system)
    loop sends: 3 lint findings, each with file, line, and rule id
    model replies: 1 unified diff touching 2 lines
    loop applies the diff to the scratch copy only
    rerun: 1 lint hit left, suite green -> keep the patch

    Budgets and stopping

    Every round costs lint runs plus model calls. A round budget caps the total before the loop starts.

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    stated example parameters: one lint run costs 2 units, one model call costs 5 units
    one round = lint + model call + rerun lint = 2 + 5 + 2 = 9 units
    3 rounds = 3 x 9 = 27 units
    split: lint 3 x 4 = 12 units, model 3 x 5 = 15 units; 12 + 15 = 27

    The loop stops at the round budget, at zero hits with a green suite, or after consecutive rounds with no gain. The exact limits vary by team and setup: measure them.

    Common mistakes

    Common pitfall

    Patching the real checkout skips the safety of the scratch copy. A bad patch then corrupts the baseline the loop compares against.

    Interview

    Likely probes: the five stages in order, and the three stop conditions.

    • Lint, diagnose, propose, apply in a scratch copy, rerun.

    • Stop at the round budget, at zero hits with green tests, or after rounds with no gain.

    • A kept patch becomes the next baseline; a dropped patch leaves no trace.

    Check yourself A round ends with zero lint hits and a green suite. What does the loop do next?

    It stops and reports success. There is nothing left to fix and the suite confirms the patch.

    Check yourself One lint run costs 2 units and one model call costs 5 units. What do 3 rounds cost under the budget above?

    27 units. Each round is 2 + 5 + 2 = 9 units, and 3 times 9 is 27.

    Related topics

    • Acceptance and tests

    • Latch inference

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    See also

    • Limits and Failure Modes
    • A Loop Simulator with a Fake Fixer