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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
  • Interview and Judgment10
    • AI Interview Questions
      • Tokens and Context Questions
      • Cost Estimation Questions
      • Prompting Questions
      • Verification Questions
      • Agent Questions
      • Guardrails and Judgment Questions
    • Design Scenarios with Numbers
  • AI Skills: Full Course Index
  • Working with AI Glossary
  • Working with AI Cheatsheet
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‹AI Interview Questions
  1. Topics
  2. AI Skills for VLSI Engineers
  3. Interview and Judgment
  4. AI Interview Questions
  5. Agent Questions

Reference: Interview and Judgment · Beginner to Intermediate

Agent Questions

Decide when to use an agent and how to bound its loop and tools.

On this page

    Question 1: when is an agent the wrong tool

    Tests whether the candidate reaches for loops only when needed. Strong answers name the single-pass alternative first.

    A fixed transform with known steps wants a script, not an agent. An agent fits when the path branches on what each tool call returns.

    Use a script

    Use an agent

    Why

    Rename signals across files

    Triage failures across logs, runs and fixes

    One has fixed steps; the other branches.

    Format a report

    Gather evidence from several tools

    No tool decision versus many.

    Replay a known flow

    Explore an unknown failure

    Replay needs no judgment; search does.

    Question 2: how do you bound the loop

    Tests step budgets and stopping rules. Strong answers give numbers and a chain-success estimate.

    Set a step budget, a per-step cost cap, and a stop test checked after every step. Longer blind chains fail fast, as the arithmetic below shows.

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    EXAMPLE parameters: each step succeeds with chance 0.9, steps independent
    5-step chain: 0.9^5 = 0.9 x 0.9 x 0.9 x 0.9 x 0.9 = 0.59049
    10-step chain: 0.9^10 = 0.59049 x 0.59049 = 0.3486784401
    shorter chains with a check after each step beat long blind chains
    example.py
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    def fake_step(state):
        # FAKE scripted stand-in for one agent step, not real behavior
        if state["checks_done"] < 2:
            state["checks_done"] += 1
            return "continue"
        return "done"
    
    
    def run_agent(step_budget):
        state = {"checks_done": 0}
        steps = 0
        while steps < step_budget:
            steps += 1
            if fake_step(state) == "done":
                return ("done", steps)
        return ("escalate", steps)
    
    
    bad = []
    status, used = run_agent(5)
    if status != "done" or used != 3:
        bad.append("should finish in 3 steps")
    status2, used2 = run_agent(2)
    if status2 != "escalate" or used2 != 2:
        bad.append("tight budget should escalate after 2 steps")
    if bad:
        print("FAIL: " + "; ".join(bad))
        raise SystemExit(1)
    print("PASS: budget 5 finishes in 3 steps, budget 2 escalates")

    The loop above is a fake stand-in, not a real agent. It proves the budget rule: enough budget finishes, a tight budget escalates instead of looping.

    Question 3: who approves side effects

    Tests separation of planning from acting. Strong answers let the agent propose and the human dispose.

    The agent may read and draft freely within its budget. Writing, deleting, sending, or merging waits for explicit human approval with the diff shown.

    Common mistakes

    Common pitfall

    Bounding retries without bounding steps still loops forever across tasks. Budget the whole run: steps, tool calls, tokens and wall time.

    Interview

    Likely follow-ups: put numbers on the bound.

    • What step budget fits this task, and what happens at zero remaining.

    • How the chain-success arithmetic changes your stopping rule.

    • Which actions need approval, and what the approver sees.

    Check yourself Each of 5 steps succeeds with chance 0.9. What is the chain success chance?

    0.59049: 0.9 to the fifth power. Ten steps fall to 0.3486784401, so check early and often.

    Check yourself The agent wants a third tool call with nothing new learned. What should the loop do?

    Stop and escalate to the human with what it found. The budget buys evidence, not motion.

    Related topics

    • Guardrails and judgment questions

    • What an agent is

    • Tool use and structured output

    • Planning, memory and stopping

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    See also

    • Tokens and Context Questions
    • Cost Estimation Questions
    • Prompting Questions