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Perplexity·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Apr 2026

Summary

Perplexity software engineering interview focused on a practical coding problem: building a Todo List API designed for AI agent consumption. The twist was thinking about extensibility from the start, not just getting CRUD working.

Questions Asked (1)

Q1

Design and implement a Todo List system meant to be consumed by an AI agent. It should support adding, listing, updating, and removing tasks, with a clean API and in-memory data model that could later be extended with things like task dependencies and cascade failure handling.

System DesignAPI & IntegrationsData Modeling
Author's notes

The CRUD part I had down fine.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then design a clean, agent-friendly API with an in-memory data model. Implement the core CRUD operations, and discuss how the design can be extended to support dependencies and cascade failure handling.

Pro tip: Emphasize idempotency and clear error handling in the API, as AI agents may retry operations; also consider using a graph structure for tasks to naturally support dependencies later.

1. Clarify Requirements

Ask about expected scale, concurrency, persistence needs, and specific agent interaction patterns (e.g., batch operations, retries).

2. Design API

Define RESTful endpoints or RPC methods for add, list, update, remove, ensuring they are intuitive and idempotent where possible.

3. Model Data

Choose in-memory structures (e.g., hash map for tasks, adjacency list for dependencies) that allow efficient CRUD and future extensions.

4. Implement Core Logic

Write code for the operations, handling edge cases like non-existent tasks and validation; include basic error responses.

5. Discuss Extensibility

Explain how to add dependencies (e.g., task graph) and cascade failure handling (e.g., marking dependent tasks as blocked) without major refactoring.

Key Points to Mention

  • Idempotent operations for agent reliability
  • Clear error codes and messages for agent parsing
  • In-memory data structures (e.g., dictionaries, graphs) for efficiency
  • API versioning or extensibility points for future features
  • Concurrency considerations (e.g., thread safety) if applicable
  • Testing strategy for agent interactions

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.