← Anthropic Interview Insights
Start by clarifying the task and constraints, then outline a modular agent loop: define tools, call the LLM, parse its output, execute tools, and repeat until a termination condition. Emphasize robustness, error handling, and observability throughout.
Pro tip: Mention that you'd log every LLM request/response and tool call for debugging and evaluation, and that you'd set a max iteration limit to prevent infinite loops.
Ask about the task type, available tools, LLM API specifics, and any latency/cost constraints. Confirm the expected termination conditions.
Specify each tool's name, description, and parameter schema in a format the LLM can understand (e.g., JSON schema). Ensure tools are idempotent and return structured results.
Outline the loop: send conversation history and tool definitions to the LLM, parse the response for tool calls or final answer, execute tools, append results, and repeat.
Use a robust parser (e.g., regex or JSON) to extract tool calls and arguments. Handle malformed outputs, API errors, and tool failures with retries or fallbacks.
Set conditions for termination: explicit final answer, max iterations, timeout, or error threshold. Include logging and monitoring for each step.
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They asked me to justify my choices after I coded it up.
Start by briefly describing a specific agent you designed and the prompting techniques you used, then dive into the trade-offs between them. Focus on how you evaluated and chose techniques based on task requirements, and conclude with lessons learned or how you'd approach it differently next time.
Pro tip: Show that you understand prompting is not one-size-fits-all: mention that you A/B tested techniques and measured performance against clear metrics, which demonstrates a rigorous, empirical approach valued at Anthropic.
Briefly describe the agent's purpose, the task, and the constraints (e.g., latency, cost, accuracy) to ground your choices.
Name 2-3 specific prompting techniques (e.g., few-shot, chain-of-thought, ReAct) and explain why you chose each for that agent.
For each technique, discuss pros and cons in terms of performance, cost, latency, reliability, and complexity.
Explain how you measured effectiveness (e.g., success rate, token usage) and iterated to find the best balance.
Summarize key takeaways and how you'd apply them to future agent designs, showing adaptability.
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Start by defining what 'correct' and 'robust' mean for your specific agent, then outline a multi-layered evaluation strategy that combines offline testing, online experimentation, and continuous monitoring. Emphasize the importance of realistic failure scenarios and iterative improvement based on data.
Pro tip: Frame your answer around the principle of 'test like you fly'—simulate production conditions as closely as possible, including edge cases and adversarial inputs, and always have a rollback plan. Show that you think about evaluation as an ongoing process, not a one-time gate.
Clearly specify what correct and robust mean for your agent in terms of functional accuracy, safety, latency, and reliability. Enumerate potential failure modes, including edge cases, adversarial attacks, and distribution shifts.
Develop a diverse set of test cases covering normal, edge, and adversarial scenarios. Use metrics like precision, recall, F1, and robustness scores, and perform error analysis to identify weaknesses.
Deploy the agent in a controlled production environment using A/B tests or canary releases. Compare against baselines or previous versions, measuring both business and system-level metrics.
Implement real-time monitoring for performance drift, anomalies, and failures. When issues arise, conduct root cause analysis to understand and fix underlying problems.
Use insights from evaluations to refine the agent, update test suites, and enhance robustness. Establish a feedback loop for ongoing improvement.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.