Why do agent handoffs fail even when each specialist prompt looks good?

Instruction: Explain common causes of bad handoffs between specialist agents.

Context: Checks whether the candidate can explain the core concept clearly and connect it to real production decisions. Explain common causes of bad handoffs between specialist agents.

Updated

Example Answer

The way I'd think about it is this: Handoffs fail because the problem is rarely the individual prompt alone. They fail when responsibility is ambiguous, state is incomplete, or each specialist is locally reasonable but globally misaligned about what done actually means.

A good handoff needs a clear contract: what task is being transferred, what context is authoritative, what success condition the next agent owns, and when the task should escalate instead of bounce again. Without that, specialists often keep reinterpreting the same job.

This is why multi-agent systems can look elegant in diagrams and chaotic in production. The prompts may be fine. The protocol between them is what is broken.

What matters in an interview is not only knowing the definition, but being able to connect it back to how it changes modeling, evaluation, or deployment decisions in practice.

Common Poor Answer

A weak answer is blaming handoff failures mostly on weak prompts. In many systems, the bigger issue is missing ownership and bad state transfer.

Your preparation path

Work through these questions in order. Read the answer aloud, then explain it in your own words.

1. Start with the foundations

Build the vocabulary and explain the core decisions.

  1. When would you use a deterministic workflow instead of an agent? Free sample
  2. What state should persist between agent turns? Free sample
  3. How do you decide whether an agent should ask a human for approval? Free sample

2. Apply it to a real workflow

Practice diagnosis, validation, and everyday tradeoffs.

  1. How would you keep an agent from looping on the same failed action? Free sample
  2. When do multi-agent systems create more risk than value? Member answer
  3. An agent keeps choosing the wrong skill. How would you diagnose and fix its catalog? Member answer

3. Prepare for senior discussions

Explain failure boundaries, recovery, and production choices.

  1. Design an agent that can resume after partial failure without repeating side effects. Member answer
  2. After a model upgrade, how would you decide which agent-harness rules to remove? Member answer
  3. An agent's execution container dies halfway through a task. What survives, and how does it recover? Member answer

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