AI & Technical question
Across many agentic coding tasks, Claude Code shows a recurring failure mode like looping, weak planning, or bad tool selection. How would you isolate whether the issue is in the base model, prompting, tool interfaces, or task decomposition, and what reusable infrastructure would you build to catch and prevent this class of regressions?
- Anthropic
- AI & Technical
- Hard
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What this question tests
Tests ability to isolate the layer causing a recurring agentic failure mode and design reusable infrastructure to catch similar regressions in the future.
How to approach it
- Collect a sample of failing transcripts showing the looping, weak planning, or bad tool selection pattern and categorize them by likely cause.
- Test the base model hypothesis by running the same tasks with different prompting and tool setups to see if the failure persists regardless of scaffolding.
- Test the prompting and tool interface hypothesis by varying tool descriptions or instructions while holding the model constant.
- Test the task decomposition hypothesis by checking whether the failure clusters around tasks needing many sequential steps versus simple ones.
- Build reusable infrastructure once the layer is isolated, such as an automated eval suite that replays this failure pattern on every new model or prompt change.
What a strong answer includes
- Uses controlled variable isolation, holding the model constant while varying prompts and tools, to actually pinpoint the layer at fault.
- Categorizes failing transcripts by pattern first, since looping, weak planning, and bad tool selection may each have different root causes.
- Proposes a specific piece of reusable infrastructure, an automated regression suite that replays this failure class, not just a one time fix.
- Frames the investment as preventing future regressions, not only fixing the current instance of the problem.
Common mistakes
- Fixing the symptom in one prompt or tool without isolating the actual layer at fault, so the failure resurfaces elsewhere.
- Building a one off patch instead of a reusable regression suite that catches the same failure class in future models.
Likely follow-up questions
- How would you decide the regression suite is comprehensive enough?
- What would you do if the failure only appeared with certain tool combinations?
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More questions from Anthropic
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 1: Foundations: the model and the decisions it forces on you
- Chapter 8: Evals: define good and make the number defensible
- Chapter 6: Agents and agentic architecture