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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
    • When to Split a Task
    • Roles and Task Contracts
    • Context and Handoffs
    • Merge and Disagreements
    • Permissions, Budgets and Failures
    • Review Team Lab
  • 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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‹Multi-Agent Design
  1. Topics
  2. AI Skills for VLSI Engineers
  3. Multi-Agent Design
  4. When to Split a Task

Multi-Agent Design · Beginner

When to Split a Task

Choose a team only after defining a fair single-assistant baseline.

On this page

    Problem: more reviewers can mean more work

    You have a short enable-register specification and twenty lines of RTL. Asking three assistants to inspect it sounds thorough. However, each assistant must understand the same reset convention, and someone must resolve duplicate or conflicting findings. For a beginner, the useful question is whether a role split removes a specific bottleneck. Counting agents is not a measure of engineering quality, and a polished team report can still miss the only important defect.

    Begin with one assistant receiving the whole approved packet and the same output contract planned for the team. Ask for requirement-to-code mappings, concrete counterexamples, unresolved questions and source references. Include human review time in this baseline. A model drafting an answer is only one part of the job: checking its assertions and correcting its unsupported claims may dominate the effort. Freeze the task and acceptance criteria before comparing alternatives.

    Worked synthetic decision

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    Synthetic planning packet; no model calls were made:
    S1: q is cleared when active-low reset is sampled low on a rising edge.
    S2: otherwise q captures d when en is high.
    S3: otherwise q retains its previous value.
    Candidate split: spec reviewer extracts obligations;
    RTL reviewer traces assignments; coordinator reconciles evidence.
    Baseline: one assistant performs all three steps using the same packet.

    The spec and RTL inspections can begin separately because neither reviewer needs the other's conclusion to read its source. The coordinator does depend on both outputs. This is a possible split, not a reason to adopt it. If the packet fits comfortably in one review, handoff overhead may outweigh any gain. If different files require distinct expertise, narrow roles may help people inspect the result, even when total token use increases.

    For an STA task, one role might extract the timing requirements while another reads the report conditions. For PD, distinguish placement observations from routing or signoff claims. For DV, separate the expected behavior from the testbench's actual checks. In every case, the common source packet must preserve units, revision and scope. A short excerpt cannot establish full-chip correctness, and another agent cannot recreate missing corner information by agreeing with a colleague.

    Exercise: make the choice testable

    Write a two-sentence experiment proposal for the enable-register review. Name the single baseline, the proposed split, a shared quality gate and two effort measures. Then decide whether ten identical report rows should be distributed to ten assistants or parsed with one fixed rule. Do this before opening the answer. Keep any expected benefit as a hypothesis; do not assign an invented improvement percentage.

    Check yourself What would a defensible proposal and initial decision contain?

    Compare one assistant with spec/RTL/coordinator roles on the same frozen packet. A human checks each requirement mapping and reproduces every claimed counterexample. Measure total human minutes and all model usage or local resource cost, plus elapsed time. Ten identical rows with fixed checks usually need a parser, not ten agents. Start with the baseline and adopt the team only if actual measured benefits justify its overhead.

    Limits and reading

    A tiny classroom packet cannot predict performance on a different design family. Repeated agents using the same model and prompt may share mistakes; separate role names do not make review independent. Use source inspection or a suitable approved engineering check as the acceptance gate. For broader evaluation planning, see NIST AI RMF 1.0, January 2023. The experiment design here is a course exercise, not a benchmark result.

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    See also

    • Context and Handoffs
    • Merge and Disagreements
    • Permissions, Budgets and Failures