Meta·Software Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

Meta Research Scientist interview that was essentially a coding exercise around building an agent loop from scratch. One meaty implementation question, no fluff, and they clearly wanted to see how you think through control flow and error handling under pressure.

Questions Asked (1)

Implement a ReAct-style agent loop using a provided set of APIs and helper functions. The loop should cycle through reasoning, action selection, tool invocation, and result observation until a final answer is reached. You also need to handle termination conditions and error cases.

System DesignAPI & IntegrationsTechnical Trade-offs

Author's notes

This one took me a minute to even parse what they were asking.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the agent's state representation and the available APIs, then outline the loop structure with clear termination conditions. Emphasize modularity, error handling, and observability, and discuss trade-offs like max iterations and fallback strategies.

Pro tip: Proactively discuss how you'd prevent infinite loops and handle tool failures gracefully—this shows production-level thinking. Also, mention that you'd log each step for debugging and monitoring, which is crucial for agent reliability.

1. Clarify Requirements and Constraints

Ask about the APIs, expected input/output, and any constraints like max iterations or timeout. Confirm the agent's goal and termination criteria.

2. Define State and Loop Structure

Outline the agent's state (e.g., history, current thought, action) and the loop: reason, select action, invoke tool, observe result, repeat until final answer.

3. Implement Termination and Error Handling

Specify conditions for termination (e.g., final answer, max steps, timeout) and how to handle errors (e.g., retry, fallback, abort with error).

4. Discuss Trade-offs and Optimizations

Talk about trade-offs like iteration limits vs. completeness, synchronous vs. asynchronous tool calls, and caching or parallelization opportunities.

5. Summarize and Validate

Recap the design, highlighting modularity and robustness, and invite feedback or questions to ensure alignment.

Key Points to Mention

State management: maintaining conversation history, current reasoning, and action space.
Termination conditions: final answer detection, max iterations, timeout, and error thresholds.
Error handling: retries with backoff, fallback actions, and graceful degradation.
Observability: logging each step, metrics for loop duration and success rate.
Modularity: separating reasoning, action selection, and tool invocation for testability.
Trade-offs: latency vs. accuracy, cost of tool calls, and complexity of error recovery.

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